diff --git a/404.html b/404.html
index 6a15fd99..bccbf825 100644
--- a/404.html
+++ b/404.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="https://ucd-serg.github.io/serocalculator/index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html
index dc9e0871..7af7d10c 100644
--- a/CODE_OF_CONDUCT.html
+++ b/CODE_OF_CONDUCT.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html
index cf15ec06..f9d90db9 100644
--- a/CONTRIBUTING.html
+++ b/CONTRIBUTING.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/enteric_fever_example.html b/articles/enteric_fever_example.html
index 08876f03..42c9455f 100644
--- a/articles/enteric_fever_example.html
+++ b/articles/enteric_fever_example.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/index.html b/articles/index.html
index 1cea7f15..3837fcf7 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/scrubTyphus_example.html b/articles/scrubTyphus_example.html
index cf1a7284..f5176f28 100644
--- a/articles/scrubTyphus_example.html
+++ b/articles/scrubTyphus_example.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/serocalculator.html b/articles/serocalculator.html
index e3f9601c..5b318536 100644
--- a/articles/serocalculator.html
+++ b/articles/serocalculator.html
@@ -28,7 +28,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/simulate_xsectionalData.html b/articles/simulate_xsectionalData.html
index 432761dc..2d1cca6f 100644
--- a/articles/simulate_xsectionalData.html
+++ b/articles/simulate_xsectionalData.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -386,7 +386,7 @@ <h3 id="estimate-incidence">estimate incidence<a class="anchor" aria-label="anch
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Elapsed time:</span></span>
 <span><span class="co">#&gt;    user  system elapsed </span></span>
-<span><span class="co">#&gt;   1.541   0.004   1.544</span></span></code></pre></div>
+<span><span class="co">#&gt;   1.554   0.000   1.554</span></span></code></pre></div>
 <p>We can extract summary statistics with <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code>:</p>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">est1</span><span class="op">)</span></span>
@@ -508,7 +508,7 @@ <h3 id="estimate-incidence-in-each-cluster">Estimate incidence in each cluster<a
 <span><span class="co">#&gt; Setting up parallel processing with `num_cores` = 3.</span></span>
 <span><span class="co">#&gt; Elapsed time for parallelized code:</span></span>
 <span><span class="co">#&gt;    user  system elapsed </span></span>
-<span><span class="co">#&gt;   0.245   0.052  93.534</span></span></code></pre></div>
+<span><span class="co">#&gt;   0.268   0.048  93.030</span></span></code></pre></div>
 <p><code>summary(ests)</code> produces a <code><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble()</a></code> with some
 extra meta-data:</p>
 <div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
diff --git a/authors.html b/authors.html
index f8a2db17..29523eda 100644
--- a/authors.html
+++ b/authors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -81,13 +81,13 @@ <h2 id="citation">Citation</h2>
 
       <p>Teunis P, Lai K, Aiemjoy K, Morrison D (2024).
 <em>serocalculator: Estimating Infection Rates from Serological Data</em>.
-R package version 0.5.0, https://ucd-serg.github.io/serocalculator/, <a href="https://github.com/UCD-SERG/serocalculator" class="external-link">https://github.com/UCD-SERG/serocalculator</a>. 
+R package version 1.0.0, https://ucd-serg.github.io/serocalculator/, <a href="https://github.com/UCD-SERG/serocalculator" class="external-link">https://github.com/UCD-SERG/serocalculator</a>. 
 </p>
       <pre>@Manual{,
   title = {serocalculator: Estimating Infection Rates from Serological Data},
   author = {Peter Teunis and Kristina Lai and Kristen Aiemjoy and Douglas Ezra Morrison},
   year = {2024},
-  note = {R package version 0.5.0, https://ucd-serg.github.io/serocalculator/},
+  note = {R package version 1.0.0, https://ucd-serg.github.io/serocalculator/},
   url = {https://github.com/UCD-SERG/serocalculator},
 }</pre>
     </div>
diff --git a/index.html b/index.html
index 1f9e365a..342c82f9 100644
--- a/index.html
+++ b/index.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/news/index.html b/news/index.html
index 1f81c37c..2ebfa077 100644
--- a/news/index.html
+++ b/news/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -55,6 +55,10 @@
       <small>Source: <a href="https://github.com/UCD-SERG/serocalculator/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="1.0.0" id="serocalculator-100">serocalculator 1.0.0<a class="anchor" aria-label="anchor" href="#serocalculator-100"></a></h2>
+<ul><li>Moved underlying methods to <code>serocalculator</code> vignette</li>
+</ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.5.0" id="serocalculator-050">serocalculator 0.5.0<a class="anchor" aria-label="anchor" href="#serocalculator-050"></a></h2>
 <ul><li><p>Spell-checking of function documentation and tutorial articles.</p></li>
diff --git a/pkgdown.yml b/pkgdown.yml
index 67a4d2f2..f57adaea 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -6,7 +6,7 @@ articles:
   scrubTyphus_example: scrubTyphus_example.html
   serocalculator: serocalculator.html
   simulate_xsectionalData: simulate_xsectionalData.html
-last_built: 2024-03-12T22:18Z
+last_built: 2024-03-12T23:43Z
 urls:
   reference: https://ucd-serg.github.io/serocalculator/reference
   article: https://ucd-serg.github.io/serocalculator/articles
diff --git a/reference/ab.html b/reference/ab.html
index 1fafbdaf..76264e45 100644
--- a/reference/ab.html
+++ b/reference/ab.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/autoplot.curve_params.html b/reference/autoplot.curve_params.html
index fe535fb2..8df83091 100644
--- a/reference/autoplot.curve_params.html
+++ b/reference/autoplot.curve_params.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/autoplot.pop_data.html b/reference/autoplot.pop_data.html
index f1ce10c6..b0205e29 100644
--- a/reference/autoplot.pop_data.html
+++ b/reference/autoplot.pop_data.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/autoplot.seroincidence.by.html b/reference/autoplot.seroincidence.by.html
index 50a31ba6..95145c9f 100644
--- a/reference/autoplot.seroincidence.by.html
+++ b/reference/autoplot.seroincidence.by.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/autoplot.seroincidence.html b/reference/autoplot.seroincidence.html
index ca287a45..a190e1ea 100644
--- a/reference/autoplot.seroincidence.html
+++ b/reference/autoplot.seroincidence.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/autoplot.summary.seroincidence.by.html b/reference/autoplot.summary.seroincidence.by.html
index 026727ce..126276a3 100644
--- a/reference/autoplot.summary.seroincidence.by.html
+++ b/reference/autoplot.summary.seroincidence.by.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/check_pop_data.html b/reference/check_pop_data.html
index 1838584b..4fab7759 100644
--- a/reference/check_pop_data.html
+++ b/reference/check_pop_data.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/clean_pop_data.html b/reference/clean_pop_data.html
index 0d668e62..3fec56d9 100644
--- a/reference/clean_pop_data.html
+++ b/reference/clean_pop_data.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/df.to.array.html b/reference/df.to.array.html
index eb334769..126518bb 100644
--- a/reference/df.to.array.html
+++ b/reference/df.to.array.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/dot-nll.html b/reference/dot-nll.html
index 03960dc5..be6dcdf4 100644
--- a/reference/dot-nll.html
+++ b/reference/dot-nll.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/est.incidence.by.html b/reference/est.incidence.by.html
index 2ab0a55f..a41b9b9e 100644
--- a/reference/est.incidence.by.html
+++ b/reference/est.incidence.by.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/est.incidence.html b/reference/est.incidence.html
index c0392b4a..4ba6bd1a 100644
--- a/reference/est.incidence.html
+++ b/reference/est.incidence.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/fdev.html b/reference/fdev.html
index c5c63c1e..084da910 100644
--- a/reference/fdev.html
+++ b/reference/fdev.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/getAdditionalData.html b/reference/getAdditionalData.html
index 32c28da7..d1b6ee09 100644
--- a/reference/getAdditionalData.html
+++ b/reference/getAdditionalData.html
@@ -20,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/graph.curve.params.html b/reference/graph.curve.params.html
index 87cccb19..289a1355 100644
--- a/reference/graph.curve.params.html
+++ b/reference/graph.curve.params.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">serocalculator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.5.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/graph.loglik.html b/reference/graph.loglik.html
index ebd49af1..6a9937fb 100644
--- a/reference/graph.loglik.html
+++ b/reference/graph.loglik.html
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index e9e821b2..c9e31de2 100644
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diff --git a/search.json b/search.json
index 0b2998f7..6e9a20d1 100644
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-[{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. 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Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement . complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to serocalculator","title":"Contributing to serocalculator","text":"outlines propose change serocalculator. detailed discussion contributing tidyverse packages, please see development contributing guide code review principles.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to serocalculator","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to serocalculator","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed). See guide create great issue advice.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to serocalculator","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"UCD-SERG/serocalculator\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to serocalculator","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to serocalculator","text":"Please note serocalculator project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Enteric Fever Seroincidence Vignette","text":"vignette provides users example analysis using serocalculator package reproducing analysis : Estimating typhoid incidence community-based serosurveys: multicohort study (Aiemjoy et al. (2022)). review methods underlying analysis walk example enteric fever incidence Pakistan.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"example-enteric-fever","dir":"Articles","previous_headings":"","what":"Example: Enteric Fever","title":"Enteric Fever Seroincidence Vignette","text":"example, users determine seroincidence enteric fever cross-sectional serosurveys conducted part SErologic Environmental Surveillance (SEES) enteric fever study Bangladesh, Nepal, Pakistan. Longitudinal antibody responses modeled 1420 blood culture-confirmed enteric fever cases enrolled countries. details published study can found : https://doi.org/10.1016/S2666-5247(22)00114-8.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"load-packages","dir":"Articles","previous_headings":"Example: Enteric Fever","what":"Load packages","title":"Enteric Fever Seroincidence Vignette","text":"first step conducting analysis load necessary packages. haven’t installed already, need loading.","code":"#devtools::install_github(\"ucd-serg/serocalculator\") library(serocalculator) #install.packages(\"tidyverse) library(tidyverse)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"load-data","dir":"Articles","previous_headings":"Example: Enteric Fever","what":"Load data","title":"Enteric Fever Seroincidence Vignette","text":"Pathogen-specific sample datasets, noise parameters, longitudinal antibody dynamics serocalculator available Serocalculator Repository Open Science Framework (OSF). pull data directly R environment. Note dataset specific formatting variable name requirements.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"load-and-prepare-longitudinal-parameter-data","dir":"Articles","previous_headings":"Example: Enteric Fever > Load data","what":"Load and prepare longitudinal parameter data","title":"Enteric Fever Seroincidence Vignette","text":"first load longitudinal curve parameters set antibody decay parameters. example, parameters modeled Bayesian hierarchical models fit two-phase power-function decay models longitudinal antibody responses among confirmed enteric fever cases. Formatting Specifications: Data imported “wide” dataframe one column parameter one row iteration posterior distribution antigen isotype. Column names must exactly match follow naming conventions: Note variable names case-sensitive","code":"#Import longitudinal antibody parameters from OSF  curve =    \"https://osf.io/download/rtw5k/\" %>%    load_curve_params()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"visualize-curve-parameters","dir":"Articles","previous_headings":"Example: Enteric Fever > Load data","what":"Visualize curve parameters","title":"Enteric Fever Seroincidence Vignette","text":"can graph decay curves autoplot() method:","code":"curve %>% autoplot()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"load-and-prepare-cross-sectional-data","dir":"Articles","previous_headings":"Example: Enteric Fever > Load data","what":"Load and prepare cross-sectional data","title":"Enteric Fever Seroincidence Vignette","text":"Next, load sample cross-sectional data. use subset results SEES dataset. Ideally, representative sample general population without regard disease status. Later, limit analysis cross-sectional data Pakistan. selected hemolysin E (HlyE) target antigen IgG IgA target immunoglobulin isotypes. Users may select different serologic markers depending available data. original dataset, rename result age variables names required serocalculator. Formatting Specifications: Cross-sectional population data “long” dataframe one column variable one row antigen isotype resulted individual. individual one row results one antigen isotype. dataframe can additional variables, two required: Note variable names case sensitive","code":"#Import cross-sectional data from OSF and rename required variables xs_data <-    \"https://osf.io/download//n6cp3/\" %>%    load_pop_data() %>%    clean_pop_data()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"check-formatting","dir":"Articles","previous_headings":"Example: Enteric Fever > Load data","what":"Check formatting","title":"Enteric Fever Seroincidence Vignette","text":"can check xs_data correct formatting using check_pop_data() function:","code":"xs_data %>% check_pop_data() #> data format is as expected."},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"summarize-antibody-data","dir":"Articles","previous_headings":"Example: Enteric Fever > Load data","what":"Summarize antibody data","title":"Enteric Fever Seroincidence Vignette","text":"can compute numerical summaries cross-sectional antibody data summary() method pop_data objects:","code":"xs_data %>% summary() #>  #> n = 3336  #>  #> Distribution of age:  #>  #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>    0.60    5.00   10.00   10.53   15.00   25.00  #>  #> Distributions of antigen-isotype measurements: #>  #>   antigen_iso Min   1st Qu.   Median  3rd Qu.      Max # NAs #> 1    HlyE_IgA   0 0.8513305 1.736334 3.656655 133.3666     0 #> 2    HlyE_IgG   0 1.1485953 2.695958 6.737705 218.6229     0"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"visualize-antibody-data","dir":"Articles","previous_headings":"Example: Enteric Fever > Load data","what":"Visualize antibody data","title":"Enteric Fever Seroincidence Vignette","text":"examine cross-sectional antibody data visualizing distribution quantitative antibody responses. , look distribution selected antigen isotype pairs, HlyE IgA HlyE IgG, across participating countries.  see across countries, data highly skewed majority responses lower end data long tails. Let’s get better look distribution log transforming antibody response value.  log transformed, data looks much normally distributed. cases, log transformation best way visualize serologic data. Let’s also take look antibody responses change age.  plot, steeper slope indicates higher incidence. can see highest burden Bangladesh. Nepal slightly higher incidence older group (higher slope).","code":"#Create plots  xs_data %>% autoplot(strata = \"Country\") #Create log transformed plots  options(scipen = 999) xs_data %>%    autoplot(strata = \"Country\", log = TRUE) #> Warning in ggplot2::scale_x_log10(): log-10 transformation #> introduced infinite values. #> Warning: Removed 18 rows containing non-finite outside the scale range #> (`stat_density()`). #Plot antibody responses by age  ggplot(data=xs_data, aes(x=age, y=value, color=Country)) +           geom_point(size=.6, alpha=.7)+          geom_smooth(method=lm, se=FALSE)+          scale_y_log10() +          theme_bw()+           labs(             title = \"Quantitative Antibody Responses by Age\",             x = \"Age\",             y = \"Value\"           ) #> Warning in scale_y_log10(): log-10 transformation introduced infinite values. #> log-10 transformation introduced infinite values. #> Warning: Removed 18 rows containing non-finite outside the scale range #> (`stat_smooth()`)."},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"load-noise-parameters","dir":"Articles","previous_headings":"Example: Enteric Fever","what":"Load noise parameters","title":"Enteric Fever Seroincidence Vignette","text":"Next, must set conditions based assumptions data errors may need accounted . differ based background knowledge data. biological noise, \\(\\nu\\) (“nu”), represents error cross-reactivity antibodies. Measurement noise, \\(\\varepsilon\\) (“epsilon”), represents error laboratory testing process. Formatting Specifications: Noise parameter data dataframe one row antigen isotype columns noise parameter . Note variable names case-sensitive.","code":"#Import noise parameters from OSF  noise <- url(\"https://osf.io/download//hqy4v/\") %>% readRDS()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"estimate-seroincidence","dir":"Articles","previous_headings":"","what":"Estimate Seroincidence","title":"Enteric Fever Seroincidence Vignette","text":"Now ready begin estimating seroincidence. conduct two separate analyses using two distinct functions, est.incidence est.incidence., calculate overall seroincidence stratified seroincidence, respectively.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"overall-seroincidence","dir":"Articles","previous_headings":"Estimate Seroincidence","what":"Overall Seroincidence","title":"Enteric Fever Seroincidence Vignette","text":"Using function est.incidence, filter sites Pakistan define datasets cross-sectional data (pop_data), longitudinal parameters (curve_param), noise parameters (noise_param). also define antigen-isotype pairs included estimate (antigen_isos). , chosen use two antigen isotypes, users can add additional pairs available.","code":"#Using est.incidence (no strata)  est1 = est.incidence(   pop_data = xs_data %>% filter(Country == \"Pakistan\"),   curve_param = curve,    noise_param = noise %>% filter(Country == \"Pakistan\"),   antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\") )  summary(est1) #> # A tibble: 1 × 10 #>   est.start incidence.rate      SE CI.lwr CI.upr coverage log.lik iterations #>       <dbl>          <dbl>   <dbl>  <dbl>  <dbl>    <dbl>   <dbl>      <int> #> 1       0.1          0.128 0.00682  0.115  0.142     0.95  -2376.          4 #> # ℹ 2 more variables: antigen.isos <chr>, nlm.convergence.code <ord>"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"stratified-seroincidence","dir":"Articles","previous_headings":"Estimate Seroincidence","what":"Stratified Seroincidence","title":"Enteric Fever Seroincidence Vignette","text":"can also produce stratified seroincidence estimates. stratify catchment area within Pakistan, users can select stratification variable cross-sectional population dataset. warned “curve_params missing strata variables, used unstratified.” can ignored, unstratified parameters simply give us overall seroincidence estimate. Let’s visualize seroincidence estimates strata.","code":"#Using est.incidence.by (strata)  est2 = est.incidence.by(   strata = c(\"catchment\"),   pop_data = xs_data %>% filter(Country == \"Pakistan\"),   curve_params = curve,    noise_params = noise %>% filter(Country == \"Pakistan\"),   antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"),   num_cores = 8 #Allow for parallel processing to decrease run time    ) #> Warning in warn.missing.strata(data = curve_params, strata = strata %>% : curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning in warn.missing.strata(data = noise_params, strata = strata %>% : noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning in check_parallel_cores(.): This computer appears to have 4 cores #> available. `est.incidence.by()` has reduced its `num_cores` argument to 3 to #> avoid destabilizing the computer.  summary(est2) #> Seroincidence estimated given the following setup: #> a) Antigen isotypes   : HlyE_IgG, HlyE_IgA  #> b) Strata       : catchment  #>  #>  Seroincidence estimates: #> # A tibble: 2 × 13 #>   Stratum   catchment     n est.start incidence.rate      SE CI.lwr CI.upr #>   <chr>     <chr>     <int>     <dbl>          <dbl>   <dbl>  <dbl>  <dbl> #> 1 Stratum 1 aku         294       0.1          0.106 0.00767 0.0916  0.122 #> 2 Stratum 2 kgh         200       0.1          0.167 0.0133  0.143   0.196 #> # ℹ 5 more variables: coverage <dbl>, log.lik <dbl>, iterations <int>, #> #   antigen.isos <chr>, nlm.convergence.code <ord> #Plot seroincidence estimates  #Save summary(est2) as a dataframe est2df<- summary(est2)  #Create barplot (rescale incidence rate and CIs) ggplot(est2df, aes(y=catchment, x=incidence.rate*1000, fill=catchment)) +   geom_bar(stat = \"identity\", show.legend = FALSE) +   geom_errorbar(aes(xmin =CI.lwr*1000, xmax=CI.upr*1000, width=.05))+   labs(       title= \"Enteric Fever Seroincidence by Catchment Area\",         x=\"Seroincience rate per 1000 person-years\",         y=\"Catchment\"       ) +   theme_bw() +   theme(axis.text.y = element_text(size=11),         axis.text.x = element_text(size=11))"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"conclusions","dir":"Articles","previous_headings":"","what":"Conclusions","title":"Enteric Fever Seroincidence Vignette","text":"data, find overall estimated seroincidence enteric fever Pakistan 153 per 1000 person-years (95% CI: 139, 169). stratified catchment area, find area KGH higher incidence rate area AKU [204 per 1000 person-years (95% CI: 176, 237) vs. 125 per 1000 person-years (95% CI: 109, 143)].","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"acknowledgments","dir":"Articles","previous_headings":"","what":"Acknowledgments","title":"Enteric Fever Seroincidence Vignette","text":"Special thanks collaborators Aga Khan University (Karachi, Pakistan), Child Health Research Foundation (Dhaka, Bangladesh), Dhulikhel Hospital, Kathmandu University Hospital (Dhulikhel, Nepal).","code":""},{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Scrub Typhus Seroincidence Vignette","text":"vignette reproduces analysis : Estimating seroincidence scrub typhus using antibody dynamics following infection (Aiemjoy et al. (Accepted Feb 2024)).","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"methods","dir":"Articles","previous_headings":"","what":"Methods","title":"Scrub Typhus Seroincidence Vignette","text":"serocalculator R package provides rapid computationally simple method calculating seroconversion rates, originally published Simonsen et al. (2009) Teunis et al. (2012), developed subsequent publications deGraaf et al. (2014), Teunis et al. (2016), Teunis Eijkeren (2020). short, longitudinal seroresponses confirmed cases known symptom onset date assumed represent time course human serum antibodies specific pathogen. Therefore, using longitudinal antibody dynamics cross–sectional sample antibodies human population, incidence estimate can calculated.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"the-seroincidence-estimator","dir":"Articles","previous_headings":"Methods","what":"The Seroincidence Estimator","title":"Scrub Typhus Seroincidence Vignette","text":"serocalculator package designed calculate incidence seroconversion using longitudinal seroresponse characteristics. distribution serum antibody concentrations cross–sectional population sample calculated function longitudinal seroresponse frequency seroconversion (seroincidence). Given seroresponse, marginal distribution antibody concentrations can fitted cross-sectional data thereby providing means estimate seroincidence.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"scrub-typhus-seroincidence","dir":"Articles","previous_headings":"","what":"Scrub Typhus Seroincidence","title":"Scrub Typhus Seroincidence Vignette","text":"Scrub typhus, vector-borne bacterial infection, important neglected disease globally. Accurately characterizing burden challenging due non-specific symptoms limited diagnostics. Prior seroepidemiology studies struggled find consensus cutoffs permit comparing estimates across contexts time. study, present novel approach require cutoff instead uses information antibody kinetics infection estimate seroincidence. use data three cohorts scrub typhus patients Chiang Rai, Thailand, Vellore, India characterize antibody kinetics infection two population serosurveys Kathmandu valley, Nepal, Tamil Nadu, India estimate seroincidence. samples tested IgM IgG responses Orientia tsutsugamushi-derived recombinant 56-kDa antigen using commercial ELISA kits. used Bayesian hierarchical models characterize antibody responses scrub typhus infection used joint distributions peak antibody titers decay rates estimate population-level incidence rates cross-sectional serosurveys.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"load-packages","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence","what":"Load packages","title":"Scrub Typhus Seroincidence Vignette","text":"first step conducting analysis load necessary packages. haven’t installed already, need loading.","code":"#devtools::install_github(\"ucd-serg/serocalculator\") library(serocalculator) #install.packages(\"tidyverse\") library(tidyverse) #install.packages(\"mixtools\") library(mixtools)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"load-data","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence","what":"Load data","title":"Scrub Typhus Seroincidence Vignette","text":"Pathogen-specific sample datasets, noise parameters, longitudinal antibody dynamics serocalculator available Serocalculator Repository Open Science Framework (OSF). pull data directly R environment.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"load-and-prepare-longitudinal-parameter-data","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence > Load data","what":"Load and prepare longitudinal parameter data","title":"Scrub Typhus Seroincidence Vignette","text":"first load longitudinal curve parameters set antibody decay parameters. parameters modeled Bayesian hierarchical models fit two-phase power-function decay models longitudinal antibody responses among confirmed enteric fever cases.","code":"#Import longitudinal antibody parameters from OSF  curve <-     \"https://osf.io/download/u5gxh/\" %>%    load_curve_params()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"visualize-curve-parameters","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence > Load data","what":"Visualize curve parameters","title":"Scrub Typhus Seroincidence Vignette","text":"can graph decay curves autoplot() method:","code":"curve %>% autoplot()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"load-and-prepare-cross-sectional-data","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence > Load data","what":"Load and prepare cross-sectional data","title":"Scrub Typhus Seroincidence Vignette","text":"Next, load sample cross-sectional data.","code":"#Import cross-sectional data from OSF and rename required variables xs_data <-    \"https://osf.io/download/h5js4/\" %>%    load_pop_data() %>%    clean_pop_data()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"check-formatting","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence > Load data","what":"Check formatting","title":"Scrub Typhus Seroincidence Vignette","text":"can check xs_data correct formatting using check_pop_data() function:","code":"xs_data %>% check_pop_data() #> data format is as expected."},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"summarize-antibody-data","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence > Load data","what":"Summarize antibody data","title":"Scrub Typhus Seroincidence Vignette","text":"can compute numerical summaries cross-sectional antibody data summary() method pop_data objects:","code":"xs_data %>% summary() #>  #> n = 1608  #>  #> Distribution of age:  #>  #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>    0.90   10.18   21.00   29.02   46.25   87.00  #>  #> Distributions of antigen-isotype measurements: #>  #>   antigen_iso   Min 1st Qu. Median 3rd Qu.   Max # NAs #> 1 OT56kda_IgG 0.050 0.25025 0.4345 0.67875 3.814     0 #> 2 OT56kda_IgM 0.051 0.10000     NA 0.22900 3.599  1105"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"visualize-antibody-data","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence > Load data","what":"Visualize antibody data","title":"Scrub Typhus Seroincidence Vignette","text":"Let’s also take look antibody responses change age.","code":"#Plot antibody responses by age  ggplot(data=xs_data, aes(x=age, y=value, color=country)) +           geom_point(size=.6, alpha=.7)+          geom_smooth(method=lm, se=FALSE)+          scale_y_log10() +          theme_bw()+           labs(             title = \"Quantitative Antibody Responses by Age\",             x = \"Age\",             y = \"Value\"           ) +    facet_wrap(~antigen_iso) +   scale_color_manual(values = c(\"orange2\",'#39558CFF')) #> Warning: Removed 1105 rows containing non-finite outside the scale range #> (`stat_smooth()`). #> Warning: Removed 1105 rows containing missing values or values outside the scale range #> (`geom_point()`)."},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"compile-noise-parameters","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence","what":"Compile noise parameters","title":"Scrub Typhus Seroincidence Vignette","text":"Next, must set conditions based assumptions data errors may need accounted . differ based background knowledge data. biological noise, \\(\\nu\\) (“nu”), represents error cross-reactivity antibodies. Measurement noise, \\(\\varepsilon\\) (“epsilon”), represents error laboratory testing process. Formatting Specifications: Noise parameter data dataframe one row antigen isotype columns noise parameter . Note variable names case-sensitive.","code":"#biologic noise b.noise <- xs_data %>%   group_by(antigen_iso) %>%   filter(!is.na(value)) %>%   filter(age<40) %>% # restrict to young ages to capture recent exposures    do({     set.seed(54321)     # Fit the mixture model     mixmod <- normalmixEM(.$value, k = 2, maxit = 1000) # k is the number of components, adjust as necessary     # Assuming the first component is the lower distribution     lower_mu <- mixmod$mu[1]     lower_sigma <- sqrt(mixmod$sigma[1])     # Calculate the 90th percentile of the lower distribution     percentile75 <- qnorm(0.75, lower_mu, lower_sigma)     # Return the results     data.frame(antigen_iso = .$antigen_iso[1], percentile75 = percentile75)   }) #> number of iterations= 35  #> number of iterations= 24    # define conditional parameters noise <- data.frame(   antigen_iso = c(\"OT56kda_IgG\", \"OT56kda_IgM\"),   nu = as.numeric(c(b.noise[2,2], b.noise[1,2])),  # Biologic noise (nu)   eps = c(0.2, 0.2),                             # M noise (eps)   y.low = c(0.2, 0.2),                            # low cutoff (llod)   y.high = c(200, 200)) %>%                      # high cutoff (y.high)   mutate(across(where(is.numeric), round, digits = 2))"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"estimate-seroincidence-by-study-site","dir":"Articles","previous_headings":"","what":"Estimate Seroincidence by study site","title":"Scrub Typhus Seroincidence Vignette","text":"Now ready begin estimating seroincidence. use est.incidence.calculate stratified seroincidence rates.","code":"#Using est.incidence.by (strata)  est = est.incidence.by(   strata = c(\"country\"),   pop_data = xs_data,   curve_params = curve,    noise_params = noise,   antigen_isos = c(\"OT56kda_IgG\"),   num_cores = 8 #Allow for parallel processing to decrease run time    ) #> Warning in warn.missing.strata(data = curve_params, strata = strata %>% : curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning in warn.missing.strata(data = noise_params, strata = strata %>% : noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning in check_parallel_cores(.): This computer appears to have 4 cores #> available. `est.incidence.by()` has reduced its `num_cores` argument to 3 to #> avoid destabilizing the computer.  summary(est) #> Seroincidence estimated given the following setup: #> a) Antigen isotypes   : OT56kda_IgG  #> b) Strata       : country  #>  #>  Seroincidence estimates: #> # A tibble: 2 × 13 #>   Stratum country     n est.start incidence.rate      SE  CI.lwr CI.upr coverage #>   <chr>   <fct>   <int>     <dbl>          <dbl>   <dbl>   <dbl>  <dbl>    <dbl> #> 1 Stratu… India     721       0.1        0.0202  0.00148 0.0175  0.0233     0.95 #> 2 Stratu… Nepal    1105       0.1        0.00854 0.00107 0.00668 0.0109     0.95 #> # ℹ 4 more variables: log.lik <dbl>, iterations <int>, antigen.isos <chr>, #> #   nlm.convergence.code <ord>"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"estimate-seroincidence-by-study-site-and-age-strata","dir":"Articles","previous_headings":"","what":"Estimate Seroincidence by study site and age strata","title":"Scrub Typhus Seroincidence Vignette","text":"Now ready begin estimating seroincidence. use est.incidence.calculate stratified seroincidence rates. Let’s visualize seroincidence estimates strata.","code":"#Using est.incidence.by (strata)  est2 = est.incidence.by(   strata = c(\"country\", \"ageQ\"),   pop_data = xs_data,   curve_params = curve,    noise_params = noise,   antigen_isos = c(\"OT56kda_IgG\"),   num_cores = 8 #Allow for parallel processing to decrease run time    ) #> Warning in warn.missing.strata(data = curve_params, strata = strata %>% : curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning in warn.missing.strata(data = noise_params, strata = strata %>% : noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning in check_parallel_cores(.): This computer appears to have 4 cores #> available. `est.incidence.by()` has reduced its `num_cores` argument to 3 to #> avoid destabilizing the computer.  summary(est2) #> Seroincidence estimated given the following setup: #> a) Antigen isotypes   : OT56kda_IgG  #> b) Strata       : country, ageQ  #>  #>  Seroincidence estimates: #> # A tibble: 5 × 14 #>   Stratum   country ageQ      n est.start incidence.rate      SE  CI.lwr  CI.upr #>   <chr>     <fct>   <fct> <int>     <dbl>          <dbl>   <dbl>   <dbl>   <dbl> #> 1 Stratum 1 India   18-29    59       0.1        0.00985 0.00330 0.00511 0.0190  #> 2 Stratum 2 India   30-49   302       0.1        0.0151  0.00178 0.0120  0.0190  #> 3 Stratum 3 India   50-89   360       0.1        0.0288  0.00290 0.0236  0.0350  #> 4 Stratum 4 Nepal   0-17    876       0.1        0.00606 0.00108 0.00428 0.00860 #> 5 Stratum 5 Nepal   18-29   229       0.1        0.0144  0.00259 0.0101  0.0205  #> # ℹ 5 more variables: coverage <dbl>, log.lik <dbl>, iterations <int>, #> #   antigen.isos <chr>, nlm.convergence.code <ord> #Plot seroincidence estimates  #Save summary(est) as a dataframe estdf<- summary(est) %>%   mutate(ageQ = \"Overall\")  #Save summary(est2) as a dataframe est2df<- summary(est2)    est.comb <- rbind(estdf, est2df)  #Create barplot (rescale incidence rate and CIs) ggplot(est.comb, aes(y=ageQ, x=incidence.rate*1000, fill=country)) +   geom_bar(stat = \"identity\", position = position_dodge2(width = 0.8, preserve = \"single\")) +   geom_linerange(aes(xmin =CI.lwr*1000, xmax=CI.upr*1000),                  position = position_dodge2(width = 0.8, preserve = \"single\")) +   labs(       title= \"Enteric Fever Seroincidence by Catchment Area\",         x=\"Seroincience rate per 1000 person-years\",         y=\"Catchment\"       ) +   theme_bw() +   #facet_wrap(~country) +    theme(axis.text.y = element_text(size=11),         axis.text.x = element_text(size=11)) +    scale_fill_manual(values = c(\"orange2\",'#39558CFF'))"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"acknowledgments","dir":"Articles","previous_headings":"","what":"Acknowledgments","title":"Scrub Typhus Seroincidence Vignette","text":"gratefully acknowledge study participants valuable time interest participating studies","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"funding","dir":"Articles","previous_headings":"","what":"Funding","title":"Scrub Typhus Seroincidence Vignette","text":"work supported National Institutes Health Fogarty International Center (FIC) [K01 TW012177] National Institute Allergy Infectious Diseases (NIAID) [R21 1AI176416]","code":""},{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/articles/serocalculator.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Methodology","text":"serocalculator R package provides rapid computationally simple method calculating seroconversion rates, originally published Simonsen et al. (2009) Teunis et al. (2012), developed subsequent publications deGraaf et al. (2014), Teunis et al. (2016), Teunis Eijkeren (2020). short, longitudinal seroresponses confirmed cases known symptom onset date assumed represent time course human serum antibodies specific pathogen. Therefore, using longitudinal antibody dynamics cross–sectional sample antibodies human population, incidence estimate can calculated. details .","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/serocalculator.html","id":"a-proxy-for-infection","dir":"Articles","previous_headings":"Overview","what":"A Proxy for Infection","title":"Methodology","text":"exact time infection impossible measure individual, antibody levels measured cross–sectional population sample can translated estimate frequency seroconversions (infections) occur sampled population. presence many high antibody concentrations indicates many people population likely experienced infection recently, mostly low concentrations indicate low frequency infections sampled population. order interpret measured cross-sectional antibody concentrations terms incidence, must define antibody dynamic time understand generalized antibody response different times since infection. dynamic must quantified time include initial increase serum antibody concentration seroconversion occurs, followed gradual decrease antibodies wane. published studies, information time course serum antibody response obtained longitudinal follow–data cases symptomatic episode following infection. case, onset symptoms provides proxy time infection occurred.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/serocalculator.html","id":"the-seroincidence-estimator","dir":"Articles","previous_headings":"Overview","what":"The Seroincidence Estimator","title":"Methodology","text":"serocalculator package designed calculate incidence seroconversion using longitudinal seroresponse characteristics. distribution serum antibody concentrations cross–sectional population sample calculated function longitudinal seroresponse frequency seroconversion (seroincidence). Given seroresponse, marginal distribution antibody concentrations can fitted cross-sectional data thereby providing means estimate seroincidence.","code":""},{"path":[]},{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"load-model-parameters","dir":"Articles","previous_headings":"Simulate a single dataset","what":"load model parameters","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"","code":"library(serocalculator) library(tidyverse) #> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── #> ✔ dplyr     1.1.4     ✔ readr     2.1.5 #> ✔ forcats   1.0.0     ✔ stringr   1.5.1 #> ✔ ggplot2   3.5.0     ✔ tibble    3.2.1 #> ✔ lubridate 1.9.3     ✔ tidyr     1.3.1 #> ✔ purrr     1.0.2      #> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── #> ✖ dplyr::filter() masks stats::filter() #> ✖ dplyr::lag()    masks stats::lag() #> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors library(ggbeeswarm) # for plotting  #load in longitudinal parameters, these are modeled from all SEES cases across all ages and countries  dmcmc =   \"https://osf.io/download/rtw5k\" %>%    load_curve_params()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"visualize-antibody-decay-model","dir":"Articles","previous_headings":"Simulate a single dataset","what":"visualize antibody decay model","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"can graph individual MCMC samples posterior distribution model parameters using autoplot.curve_params() method autoplot() function:  can use logarithmic scale x-axis desired:  can graph median, 10%, 90% quantiles model using graph.curve.params() function:","code":"dmcmc %>% autoplot() dmcmc %>% autoplot(log_x = TRUE) # Specify the antibody-isotype responses to include in analyses antibodies = c(\"HlyE_IgA\", \"HlyE_IgG\")  dmcmc %>%    graph.curve.params(antigen_isos = antibodies) %>%    print() #> Warning: Removed 57 rows containing missing values or values outside the scale range #> (`geom_line()`)."},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"simulate-cross-sectional-data","dir":"Articles","previous_headings":"Simulate a single dataset","what":"Simulate cross-sectional data","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"","code":"#set seed to reproduce results set.seed(54321)  # simulated incidence rate per person-year lambda <- 0.2;  # range covered in simulations lifespan <- c(0, 10);   # cross-sectional sample size nrep <- 100  # biologic noise distribution dlims <- rbind(   \"HlyE_IgA\" = c(min = 0, max = 0.5),   \"HlyE_IgG\" = c(min = 0, max = 0.5)) #generate cross-sectional data csdata <- sim.cs(   curve_params = dmcmc,   lambda = lambda,   n.smpl = nrep,   age.rng = lifespan,   antigen_isos = antibodies,   n.mc = 0,   renew.params = TRUE,   add.noise = TRUE,   noise_limits = dlims,   format = \"long\" )"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"noise-parameters","dir":"Articles","previous_headings":"Simulate a single dataset","what":"Noise parameters","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"need provide noise parameters analysis; , define directly code:","code":"library(tibble) cond <- tibble(   antigen_iso = c(\"HlyE_IgG\", \"HlyE_IgA\"),   nu = c(0.5, 0.5),                          # Biologic noise (nu)   eps = c(0, 0),                             # M noise (eps)   y.low = c(1, 1),                           # low cutoff (llod)   y.high = c(5e6, 5e6))                      # high cutoff (y.high)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"visualize-data","dir":"Articles","previous_headings":"Simulate a single dataset","what":"Visualize data","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"can plot distribution antibody responses simulated data.","code":"ggplot(csdata, aes(x=as.factor(antigen_iso), y= value)) +   geom_beeswarm(size= .2, alpha = .3, aes(color = antigen_iso), show.legend = F) +    geom_boxplot(outlier.colour = NA, fill = NA) +    scale_y_log10() +    theme_linedraw() +    labs(x=\"antigen - isotype\")"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"calculate-log-likelihood","dir":"Articles","previous_headings":"Simulate a single dataset","what":"calculate log-likelihood","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"can calculate log-likelihood data function incidence rate directly:","code":"ll_A = llik(   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = \"HlyE_IgA\",   lambda = 0.1) %>% print() #> [1] -226.1281  ll_G = llik(   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = \"HlyE_IgG\",   lambda = 0.1) %>% print() #> [1] -327.432  ll_AG = llik(   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = c(\"HlyE_IgG\",\"HlyE_IgA\"),   lambda = 0.1) %>% print() #> [1] -553.5601  print(ll_A + ll_G) #> [1] -553.5601"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"graph-log-likelihood","dir":"Articles","previous_headings":"Simulate a single dataset","what":"graph log-likelihood","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"can also graph log-likelihoods, even without finding MLEs, using graph.loglik():","code":"lik_HlyE_IgA = graph.loglik(   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = \"HlyE_IgA\",   log_x = TRUE )  lik_HlyE_IgG = graph.loglik(   previous_plot = lik_HlyE_IgA,   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = \"HlyE_IgG\",   log_x = TRUE )  lik_both = graph.loglik(   previous_plot = lik_HlyE_IgG,   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"),   log_x = TRUE )  print(lik_both)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"estimate-incidence","dir":"Articles","previous_headings":"Simulate a single dataset","what":"estimate incidence","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"can estimate incidence est.incidence(): can extract summary statistics summary(): can plot log-likelihood curve autoplot():  can set x-axis logarithmic scale:","code":"est1 = est.incidence(   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   lambda_start = .1,   build_graph = T,   verbose = T, # print updates as the function runs   print_graph = F, # display the log-likelihood curve while `est.incidence()` is running   antigen_isos = antibodies) #> data format is as expected. #> nrow(curve_params) = 8000 #> Initial negative log-likelihood: 553.560077627804 #> building likelihood graph #> about to call `nlm()` #> iteration = 0 #> Step: #> [1] 0 #> Parameter: #> [1] -2.302585 #> Function Value #> [1] 553.5601 #> Gradient: #> [1] -42.3037 #>  #> iteration = 1 #> Step: #> [1] 0.4769564 #> Parameter: #> [1] -1.825629 #> Function Value #> [1] 539.2395 #> Gradient: #> [1] -15.99511 #>  #> iteration = 2 #> Step: #> [1] 0.2899803 #> Parameter: #> [1] -1.535648 #> Function Value #> [1] 537.6454 #> Gradient: #> [1] 5.827356 #>  #> iteration = 3 #> Step: #> [1] -0.07743479 #> Parameter: #> [1] -1.613083 #> Function Value #> [1] 537.4411 #> Gradient: #> [1] -0.4900419 #>  #> iteration = 4 #> Step: #> [1] 0.006006633 #> Parameter: #> [1] -1.607077 #> Function Value #> [1] 537.4396 #> Gradient: #> [1] -0.01308702 #>  #> iteration = 5 #> Parameter: #> [1] -1.606912 #> Function Value #> [1] 537.4396 #> Gradient: #> [1] 3.275662e-05 #>  #> Relative gradient close to zero. #> Current iterate is probably solution. #>  #> Elapsed time: #>    user  system elapsed  #>   1.541   0.004   1.544 summary(est1) #> # A tibble: 1 × 10 #>   est.start incidence.rate     SE CI.lwr CI.upr coverage log.lik iterations #>       <dbl>          <dbl>  <dbl>  <dbl>  <dbl>    <dbl>   <dbl>      <int> #> 1       0.1          0.201 0.0225  0.161  0.250     0.95   -537.          5 #> # ℹ 2 more variables: antigen.isos <chr>, nlm.convergence.code <ord> autoplot(est1) autoplot(est1, log_x = TRUE)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"simulate-multiple-clusters-with-different-lambdas","dir":"Articles","previous_headings":"","what":"Simulate multiple clusters with different lambdas","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"preceding code chunk, determined can use 3 CPU cores run computations parallel. can plot distributions simulated responses:","code":"library(parallel) n_cores = max(1, parallel::detectCores() - 1) # n_cores = 1 print(n_cores) #> [1] 3 #number of clusters  nclus = 10;  # cross-sectional sample size nrep <- 100  #incidence rate in e lmbdaVec <- c(.05,.1, .15, .2)  sim.df =    sim.cs.multi(     # verbose = TRUE,     n_cores = n_cores,     lambdas = lmbdaVec,     nclus = nclus,     n.smpl = nrep,     age.rng = lifespan,     antigen_isos = antibodies,     renew.params = TRUE,     add.noise = TRUE,     curve_params = dmcmc,     noise_limits = dlims,     format = \"long\"   )  print(sim.df) #> # A tibble: 8,000 × 5 #>      age antigen_iso value lambda.sim cluster #>    <dbl> <chr>       <dbl>      <dbl>   <int> #>  1  3.53 HlyE_IgA    0.538       0.05       1 #>  2  3.53 HlyE_IgG    0.592       0.05       1 #>  3  2.27 HlyE_IgA    0.620       0.05       1 #>  4  2.27 HlyE_IgG    0.309       0.05       1 #>  5  9.05 HlyE_IgA    0.464       0.05       1 #>  6  9.05 HlyE_IgG    0.638       0.05       1 #>  7  5.94 HlyE_IgA    0.417       0.05       1 #>  8  5.94 HlyE_IgG    0.662       0.05       1 #>  9  9.88 HlyE_IgA    0.185       0.05       1 #> 10  9.88 HlyE_IgG    0.278       0.05       1 #> # ℹ 7,990 more rows ggplot(sim.df, aes(x = as.factor(cluster), y= value)) +   geom_beeswarm(size= .2, alpha = .3, aes(color = antigen_iso)) +    geom_boxplot(outlier.colour = NA, fill = NA) +    scale_y_log10() +    facet_wrap(~antigen_iso + lambda.sim, nrow = 2) +    theme_linedraw()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"estimate-incidence-in-each-cluster","dir":"Articles","previous_headings":"Simulate multiple clusters with different lambdas","what":"Estimate incidence in each cluster","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"summary(ests) produces tibble() extra meta-data: can explore summary table interactively using DT::datatable() can plot likelihood single simulated cluster subsetting simulation ests calling plot():  can also plot log-likelihood curves several clusters (computer might struggle plot many ):  log_x argument also works :","code":"ests =   sim.df %>%    est.incidence.by(     curve_params = dmcmc,     noise_params = cond,     num_cores = n_cores,     strata = c(\"lambda.sim\", \"cluster\"),     curve_strata_varnames = NULL,     noise_strata_varnames = NULL,     verbose = TRUE,     build_graph = TRUE, # slows down the function substantially     antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\")     # antigen_isos = \"HlyE_IgA\"   ) #> data format is as expected. #> Data has been stratified. #> Here are the strata that will be analyzed: #> # A tibble: 40 × 4 #>    Stratum    lambda.sim cluster     n #>    <chr>           <dbl>   <int> <int> #>  1 Stratum 1        0.05       1   100 #>  2 Stratum 2        0.05       2   100 #>  3 Stratum 3        0.05       3   100 #>  4 Stratum 4        0.05       4   100 #>  5 Stratum 5        0.05       5   100 #>  6 Stratum 6        0.05       6   100 #>  7 Stratum 7        0.05       7   100 #>  8 Stratum 8        0.05       8   100 #>  9 Stratum 9        0.05       9   100 #> 10 Stratum 10       0.05      10   100 #> # ℹ 30 more rows #> Setting up parallel processing with `num_cores` = 3. #> Elapsed time for parallelized code: #>    user  system elapsed  #>   0.245   0.052  93.534 summary(ests) #> Warning in FUN(X[[i]], ...): `nlm()` produced a negative hessian; something is wrong with the numerical derivatives. #> The standard error of the incidence rate estimate cannot be calculated. #> Warning in sqrt(var.log.lambda): NaNs produced #> Seroincidence estimated given the following setup: #> a) Antigen isotypes   : HlyE_IgG, HlyE_IgA  #> b) Strata       : lambda.sim, cluster  #>  #>  Seroincidence estimates: #> # A tibble: 40 × 14 #>    Stratum    lambda.sim cluster     n est.start incidence.rate      SE CI.lwr #>    <chr>           <dbl>   <int> <int>     <dbl>          <dbl>   <dbl>  <dbl> #>  1 Stratum 1        0.05       1   100       0.1         0.0367 0.00686 0.0254 #>  2 Stratum 2        0.05       2   100       0.1         0.0423 0.00778 0.0295 #>  3 Stratum 3        0.05       3   100       0.1         0.0648 0.00992 0.0480 #>  4 Stratum 4        0.05       4   100       0.1         0.0665 0.0107  0.0484 #>  5 Stratum 5        0.05       5   100       0.1         0.0390 0.00769 0.0265 #>  6 Stratum 6        0.05       6   100       0.1         0.0474 0.00846 0.0334 #>  7 Stratum 7        0.05       7   100       0.1         0.0684 0.0105  0.0506 #>  8 Stratum 8        0.05       8   100       0.1         0.0309 0.00643 0.0206 #>  9 Stratum 9        0.05       9   100       0.1         0.0466 0.00827 0.0329 #> 10 Stratum 10       0.05      10   100       0.1         0.0461 0.00824 0.0325 #> # ℹ 30 more rows #> # ℹ 6 more variables: CI.upr <dbl>, coverage <dbl>, log.lik <dbl>, #> #   iterations <int>, antigen.isos <chr>, nlm.convergence.code <ord> library(DT) summary(ests) %>%    DT::datatable() %>%   DT::formatRound(     columns = c(       \"incidence.rate\",       \"SE\",       \"CI.lwr\",       \"CI.upr\",       \"log.lik\"     )   ) #> Warning in FUN(X[[i]], ...): `nlm()` produced a negative hessian; something is wrong with the numerical derivatives. #> The standard error of the incidence rate estimate cannot be calculated. #> Warning in sqrt(var.log.lambda): NaNs produced autoplot(ests[1]) autoplot(ests[1:5]) autoplot(ests[1:5], log_x = TRUE)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"nlm-convergence-codes","dir":"Articles","previous_headings":"Simulate multiple clusters with different lambdas > Estimate incidence in each cluster","what":"nlm() convergence codes","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"Make sure check nlm() exit codes (codes 3-5 indicate possible non-convergence): Solutions nlm() exit codes 3-5: 3: decrease stepmin argument est.incidence()/est.incidence.() 4: increase iterlim argument est.incidence()/est.incidence.() 5: increase stepmax argument est.incidence()/est.incidence.() can extract indices problematic strata, : clusters problems, can take look: fits don’t appear maximum likelihood, re-run clusters, adjusting nlm() settings appropriately, sure.","code":"summary(ests) %>%   as_tibble() %>% # removes extra meta-data   select(Stratum, nlm.convergence.code) %>%    filter(nlm.convergence.code > 2)  #> Warning in FUN(X[[i]], ...): `nlm()` produced a negative hessian; something is wrong with the numerical derivatives. #> The standard error of the incidence rate estimate cannot be calculated. #> Warning in sqrt(var.log.lambda): NaNs produced #> # A tibble: 0 × 2 #> # ℹ 2 variables: Stratum <chr>, nlm.convergence.code <ord> problem_strata =    which(summary(ests)$nlm.convergence.code > 2) %>%    print() #> Warning in FUN(X[[i]], ...): `nlm()` produced a negative hessian; something is wrong with the numerical derivatives. #> The standard error of the incidence rate estimate cannot be calculated. #> Warning in sqrt(var.log.lambda): NaNs produced #> integer(0) if(length(problem_strata) > 0) {   autoplot(ests[problem_strata], log_x = TRUE) }"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"plot-distribution-of-estimates-by-simulated-incidence-rate","dir":"Articles","previous_headings":"Simulate multiple clusters with different lambdas","what":"plot distribution of estimates by simulated incidence rate","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"Finally, can look simulation results:","code":"library(ggplot2) summary(ests) %>%    autoplot(xvar = 'lambda.sim') +   ggplot2::geom_abline(     ggplot2::aes(intercept = 0, slope = 1)) #> Warning in FUN(X[[i]], ...): `nlm()` produced a negative hessian; something is wrong with the numerical derivatives. #> The standard error of the incidence rate estimate cannot be calculated. #> Warning in sqrt(var.log.lambda): NaNs produced"},{"path":"https://ucd-serg.github.io/serocalculator/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Peter Teunis. Author, copyright holder.            Author method original code. Kristina Lai. Author, maintainer. Kristen Aiemjoy. Author. Douglas Ezra Morrison. Author.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Teunis P, Lai K, Aiemjoy K, Morrison D (2024). serocalculator: Estimating Infection Rates Serological Data. R package version 0.5.0, https://ucd-serg.github.io/serocalculator/, https://github.com/UCD-SERG/serocalculator.","code":"@Manual{,   title = {serocalculator: Estimating Infection Rates from Serological Data},   author = {Peter Teunis and Kristina Lai and Kristen Aiemjoy and Douglas Ezra Morrison},   year = {2024},   note = {R package version 0.5.0, https://ucd-serg.github.io/serocalculator/},   url = {https://github.com/UCD-SERG/serocalculator}, }"},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"serocalculator","dir":"","previous_headings":"","what":"Estimating Infection Rates from Serological Data","title":"Estimating Infection Rates from Serological Data","text":"Antibody levels measured cross–sectional population sample can translated estimate frequency seroconversions (infections) occur sampled population. words, presence many high antibody titers indicates many individuals likely experienced infection recently burden disease high population, low titers indicate low frequency infections sampled population therefore lower burden disease. serocalculator package designed use longitudinal response characteristics using set modeled parameters characterizing longitudinal response selected serum antibodies. details underlying methods can found Getting Started.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"installing-r","dir":"","previous_headings":"","what":"Installing R","title":"Estimating Infection Rates from Serological Data","text":"serocalculator package written R, free, open-source software program. end user package must access working installation R software. recommend installing base R Graphical User Interfaces (GUI) R RStudio. need download install R /RStudio, recommend following tutorial Hands Programming R Garrett Grolemund: Installing R RStudio: https://rstudio-education.github.io/hopr/starting.html","code":""},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"installing-the-serocalculator-package","dir":"","previous_headings":"","what":"Installing the Serocalculator Package","title":"Estimating Infection Rates from Serological Data","text":"serocalculator package must installed R first use.","code":"# Install package install.packages(\"serocalculator\")"},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"post-installation","dir":"","previous_headings":"Installing the Serocalculator Package","what":"Post-installation","title":"Estimating Infection Rates from Serological Data","text":"Successful installation can confirmed loading package RStudio workspaceand exploring help files manuals distributed package: Additionally, package details can found executing following commands:","code":"# Load package \"seroincidence\". library(serocalculator)  # Show R help for the package. ?serocalculator # Show description. packageDescription(\"serocalculator\")  # Show citation. citation(\"serocalculator\")"},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"development-version","dir":"","previous_headings":"Installing the Serocalculator Package","what":"Development Version","title":"Estimating Infection Rates from Serological Data","text":"install development version, must install devtools R package download serocalculator GitHub. Enter code R console install packages.","code":"# Install the devtools package and the development version of serocalculator install.packages(\"devtools\") devtools::install_github(\"ucd-serg/serocalculator\")"},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"a-note-for-windows-users","dir":"","previous_headings":"Installing the Serocalculator Package > Development Version","what":"A Note for Windows Users","title":"Estimating Infection Rates from Serological Data","text":"launching development version serocalculator, Windows users need install Rtools, contains collection tools building employing R packages still development. can done either devtools package installation, independently devtools already installed.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"during-devtools-installation","dir":"","previous_headings":"Installing the Serocalculator Package > Development Version > A Note for Windows Users","what":"During devtools installation:","title":"Estimating Infection Rates from Serological Data","text":"prompted install additional build tools, select “Yes” Rtools installed. Click Yes install Rtools along devtools package","code":""},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"independently","dir":"","previous_headings":"Installing the Serocalculator Package > Development Version > A Note for Windows Users","what":"Independently:","title":"Estimating Infection Rates from Serological Data","text":"Download Rtools https://cran.r-project.org/bin/windows/Rtools/ Run installer Rtools installation may see window asking “Select Additional Tasks”. select box “Edit system PATH”. devtools RStudio put Rtools PATH automatically needed. select box “Save version information registry”. selected default.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"Estimating Infection Rates from Serological Data","text":"need assistance encounter clear bug, please file issue minimal reproducible example GitHub. Another great resource Epidemiologist R Handbook, includes introductory page asking help R packages via GitHub: https://epirhandbook.com/en/getting-help.html","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/ab.html","id":null,"dir":"Reference","previous_headings":"","what":"kinetics of the antibody (ab) response (power function decay) — ab","title":"kinetics of the antibody (ab) response (power function decay) — ab","text":"kinetics antibody (ab) response (power function decay)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/ab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"kinetics of the antibody (ab) response (power function decay) — ab","text":"","code":"ab(t, par, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/ab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"kinetics of the antibody (ab) response (power function decay) — ab","text":"t age infection? par parameters ... arguments passed baseline()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/ab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"kinetics of the antibody (ab) response (power function decay) — ab","text":"matrix()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.curve_params.html","id":null,"dir":"Reference","previous_headings":"","what":"graph antibody decay curves by antigen isotype — autoplot.curve_params","title":"graph antibody decay curves by antigen isotype — autoplot.curve_params","text":"graph antibody decay curves antigen isotype","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.curve_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"graph antibody decay curves by antigen isotype — autoplot.curve_params","text":"","code":"# S3 method for curve_params autoplot(   object,   antigen_isos = object$antigen_iso %>% unique(),   ncol = min(3, length(antigen_isos)),   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.curve_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"graph antibody decay curves by antigen isotype — autoplot.curve_params","text":"object data.frame() curve parameters (one MCMC samples) antigen_isos antigen isotypes analyze (can used subset curve_params) ncol many columns subfigures use panel plot ... Arguments passed plot_curve_params_one_ab verbose verbose output xlim range x values graph n_curves many curves plot (see details). rows_to_graph rows curve_params plot (overrides n_curves). alpha (passed ggplot2::geom_function()) transparent curves : 0 = fully transparent (invisible) 1 = fully opaque log_x x-axis logarithmic scale (TRUE) linear scale (FALSE, default)?","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.curve_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"graph antibody decay curves by antigen isotype — autoplot.curve_params","text":"ggplot2::ggplot() object","code":""},{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.curve_params.html","id":"rows-to-graph","dir":"Reference","previous_headings":"","what":"rows_to_graph","title":"graph antibody decay curves by antigen isotype — autoplot.curve_params","text":"Note directly specify rows_to_graph calling function, row numbers enumerated separately antigen isotype; words, purposes argument, row numbers start 1 antigen isotype. currently way specify different row numbers different antigen isotypes; want , call plot_curve_params_one_ab() directly antigen isotype combine resulting panels . subset curve_params manually, passing function, set n_curves argument Inf.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.curve_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"graph antibody decay curves by antigen isotype — autoplot.curve_params","text":"","code":"library(dplyr) #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union library(ggplot2)  curve = \"https://osf.io/download/rtw5k/\" %>%   load_curve_params() %>%   autoplot()"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.pop_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot distribution of antibodies — autoplot.pop_data","title":"Plot distribution of antibodies — autoplot.pop_data","text":"autoplot() method pop_data objects","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.pop_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot distribution of antibodies — autoplot.pop_data","text":"","code":"# S3 method for pop_data autoplot(object, log = FALSE, strata = NULL, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.pop_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot distribution of antibodies — autoplot.pop_data","text":"object pop_data object (load_pop_data()) log whether show antibody responses logarithmic scale strata name variable pop_data stratify (NULL stratification) ... unused","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.pop_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot distribution of antibodies — autoplot.pop_data","text":"ggplot2::ggplot object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.pop_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot distribution of antibodies — autoplot.pop_data","text":"","code":"library(dplyr) library(ggplot2)  xs_data <- \"https://osf.io/download//n6cp3/\" %>% load_pop_data() %>% clean_pop_data()  xs_data %>% autoplot(strata = \"Country\")"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot seroincidence.by log-likelihoods — autoplot.seroincidence.by","title":"Plot seroincidence.by log-likelihoods — autoplot.seroincidence.by","text":"Plots log-likelihood curves stratum, seroincidence.objects","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot seroincidence.by log-likelihoods — autoplot.seroincidence.by","text":"","code":"# S3 method for seroincidence.by autoplot(object, ncol = min(3, length(object)), ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot seroincidence.by log-likelihoods — autoplot.seroincidence.by","text":"object '\"seroincidence.\"' object (est.incidence.()) ncol number columns use panel plots ... Arguments passed autoplot.seroincidence log_x x-axis logarithmic scale (TRUE) linear scale (FALSE, default)?","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot seroincidence.by log-likelihoods — autoplot.seroincidence.by","text":"object class \"ggarrange\", ggplot2::ggplot() list() ggplot2::ggplot()s.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot seroincidence.by log-likelihoods — autoplot.seroincidence.by","text":"","code":"library(dplyr) library(ggplot2)  xs_data <- \"https://osf.io/download//n6cp3/\" %>% load_pop_data() %>% clean_pop_data  curve <- \"https://osf.io/download/rtw5k/\" %>% load_curve_params()  noise <-\"https://osf.io/download//hqy4v/\" %>% load_noise_params()  est2 = est.incidence.by( strata = c(\"catchment\"), pop_data = xs_data %>% filter(Country == \"Pakistan\"), curve_params = curve, noise_params = noise %>% filter(Country == \"Pakistan\"), antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"), #num_cores = 8, #Allow for parallel processing to decrease run time build_graph = TRUE ) #> data format is as expected. #> Warning: curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning: noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> data format is as expected. #> data format is as expected.  #plot the log-likelihood curve   autoplot(est2)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the log-likelihood curve for the incidence rate estimate — autoplot.seroincidence","title":"Plot the log-likelihood curve for the incidence rate estimate — autoplot.seroincidence","text":"Plot log-likelihood curve incidence rate estimate","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the log-likelihood curve for the incidence rate estimate — autoplot.seroincidence","text":"","code":"# S3 method for seroincidence autoplot(object, log_x = FALSE, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the log-likelihood curve for the incidence rate estimate — autoplot.seroincidence","text":"object seroincidence object (est.incidence()) log_x x-axis logarithmic scale (TRUE) linear scale (FALSE, default)? ... unused","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the log-likelihood curve for the incidence rate estimate — autoplot.seroincidence","text":"ggplot2::ggplot()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the log-likelihood curve for the incidence rate estimate — autoplot.seroincidence","text":"","code":"library(dplyr) library(ggplot2)  xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") xs_data = clean_pop_data(xs_data)  curve = load_curve_params(\"https://osf.io/download/rtw5k/\") noise = load_noise_params(\"https://osf.io/download//hqy4v/\")  est1 = est.incidence(   pop_data = xs_data %>% filter(Country == \"Pakistan\"),   curve_param = curve,   noise_param = noise %>% filter(Country == \"Pakistan\"),   antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"),   build_graph = TRUE ) #> data format is as expected.  #plot the log-likelihood curve   autoplot(est1)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.summary.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot method for summary.seroincidence.by objects — autoplot.summary.seroincidence.by","title":"Plot method for summary.seroincidence.by objects — autoplot.summary.seroincidence.by","text":"Plot method summary.seroincidence.objects","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.summary.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot method for summary.seroincidence.by objects — autoplot.summary.seroincidence.by","text":"","code":"# S3 method for summary.seroincidence.by autoplot(object, xvar, alpha = 0.7, shape = 1, width = 0.001, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.summary.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot method for summary.seroincidence.by objects — autoplot.summary.seroincidence.by","text":"object summary.seroincidence.object (generated applying summary() method output est.incidence.()). xvar name stratifying variable object alpha transparency points graph (1 = transparency, 0 = fully transparent) shape shape argument geom_point() width width jitter ... unused","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.summary.seroincidence.by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot method for summary.seroincidence.by objects — autoplot.summary.seroincidence.by","text":"ggplot2::ggplot() object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.summary.seroincidence.by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot method for summary.seroincidence.by objects — autoplot.summary.seroincidence.by","text":"","code":"library(dplyr) library(ggplot2)  xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") %>%   clean_pop_data() curve = load_curve_params(\"https://osf.io/download/rtw5k/\" ) noise = load_noise_params(\"https://osf.io/download//hqy4v/\")  est2 = est.incidence.by( strata = c(\"catchment\"), pop_data = xs_data %>% filter(Country == \"Pakistan\"), curve_params = curve, noise_params = noise %>% filter(Country == \"Pakistan\"), antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"), #num_cores = 8 #Allow for parallel processing to decrease run time ) #> data format is as expected. #> Warning: curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning: noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> data format is as expected. #> data format is as expected.  est2sum <- summary(est2)  autoplot(est2sum, \"catchment\")"},{"path":"https://ucd-serg.github.io/serocalculator/reference/check_pop_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the formatting of a cross-sectional antibody survey dataset. — check_pop_data","title":"Check the formatting of a cross-sectional antibody survey dataset. — check_pop_data","text":"Check formatting cross-sectional antibody survey dataset.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/check_pop_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the formatting of a cross-sectional antibody survey dataset. — check_pop_data","text":"","code":"check_pop_data(pop_data)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/check_pop_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the formatting of a cross-sectional antibody survey dataset. — check_pop_data","text":"pop_data dataset check","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/check_pop_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the formatting of a cross-sectional antibody survey dataset. — check_pop_data","text":"NULL (invisibly)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/check_pop_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check the formatting of a cross-sectional antibody survey dataset. — check_pop_data","text":"","code":"library(dplyr)  #Import cross-sectional data from OSF and rename required variables xs_data <-  \"https://osf.io/download//n6cp3/\" %>%   load_pop_data() %>%   clean_pop_data()    xs_data %>% check_pop_data() #> data format is as expected."},{"path":"https://ucd-serg.github.io/serocalculator/reference/clean_pop_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Reformat a cross-sectional antibody survey dataset — clean_pop_data","title":"Reformat a cross-sectional antibody survey dataset — clean_pop_data","text":"Reformat cross-sectional antibody survey dataset","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/clean_pop_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reformat a cross-sectional antibody survey dataset — clean_pop_data","text":"","code":"clean_pop_data(pop_data)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/clean_pop_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reformat a cross-sectional antibody survey dataset — clean_pop_data","text":"pop_data data.frame() containing following columns: index_id: character() variable identifying multiple rows data person antigen_isos: character() variable indicating antigen-isotype measured result: measured antibody concentration Age: age individual whose serum assayed, time blood sample collection.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/clean_pop_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reformat a cross-sectional antibody survey dataset — clean_pop_data","text":"data.frame (tibble::tbl_df) containing following columns: id: character() variable identifying multiple rows data person antigen_isos: character() variable indicating antigen-isotype measured value: measured antibody concentration age: age individual whose serum assayed, time blood sample","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/clean_pop_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reformat a cross-sectional antibody survey dataset — clean_pop_data","text":"","code":"xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") clean_pop_data(xs_data)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/df.to.array.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a data.frame (or tibble) into a multidimensional array — df.to.array","title":"Convert a data.frame (or tibble) into a multidimensional array — df.to.array","text":"Convert data.frame (tibble) multidimensional array","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/df.to.array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a data.frame (or tibble) into a multidimensional array — df.to.array","text":"","code":"df.to.array(df, dim_var_names, value_var_name = \"value\")"},{"path":"https://ucd-serg.github.io/serocalculator/reference/df.to.array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a data.frame (or tibble) into a multidimensional array — df.to.array","text":"df data.frame() (tibble::tibble()) long format (row contains one value intended array) dim_var_names character() vector variable names df. variables factors, warning produced. value_var_name character() variable containing variable name df contains values intended array.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/df.to.array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a data.frame (or tibble) into a multidimensional array — df.to.array","text":"array() dimensions defined variables df listed dim_var_names","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/df.to.array.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a data.frame (or tibble) into a multidimensional array — df.to.array","text":"","code":"library(dplyr) library(tidyr) df = iris %>%   tidyr::pivot_longer(   names_to = \"parameter\",   cols = c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Width\", \"Petal.Length\") ) %>%   mutate(parameter = factor(parameter, levels = unique(parameter)))   df %>% serocalculator:::df.to.array(dim_var_names = c(\"parameter\", \"Species\")) #> , , obs = 1 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        7.0       6.3 #>   Sepal.Width     3.5        3.2       3.3 #>   Petal.Width     0.2        1.4       2.5 #>   Petal.Length    1.4        4.7       6.0 #>  #> , , obs = 2 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.9        6.4       5.8 #>   Sepal.Width     3.0        3.2       2.7 #>   Petal.Width     0.2        1.5       1.9 #>   Petal.Length    1.4        4.5       5.1 #>  #> , , obs = 3 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.7        6.9       7.1 #>   Sepal.Width     3.2        3.1       3.0 #>   Petal.Width     0.2        1.5       2.1 #>   Petal.Length    1.3        4.9       5.9 #>  #> , , obs = 4 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.6        5.5       6.3 #>   Sepal.Width     3.1        2.3       2.9 #>   Petal.Width     0.2        1.3       1.8 #>   Petal.Length    1.5        4.0       5.6 #>  #> , , obs = 5 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        6.5       6.5 #>   Sepal.Width     3.6        2.8       3.0 #>   Petal.Width     0.2        1.5       2.2 #>   Petal.Length    1.4        4.6       5.8 #>  #> , , obs = 6 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.4        5.7       7.6 #>   Sepal.Width     3.9        2.8       3.0 #>   Petal.Width     0.4        1.3       2.1 #>   Petal.Length    1.7        4.5       6.6 #>  #> , , obs = 7 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.6        6.3       4.9 #>   Sepal.Width     3.4        3.3       2.5 #>   Petal.Width     0.3        1.6       1.7 #>   Petal.Length    1.4        4.7       4.5 #>  #> , , obs = 8 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        4.9       7.3 #>   Sepal.Width     3.4        2.4       2.9 #>   Petal.Width     0.2        1.0       1.8 #>   Petal.Length    1.5        3.3       6.3 #>  #> , , obs = 9 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.4        6.6       6.7 #>   Sepal.Width     2.9        2.9       2.5 #>   Petal.Width     0.2        1.3       1.8 #>   Petal.Length    1.4        4.6       5.8 #>  #> , , obs = 10 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.9        5.2       7.2 #>   Sepal.Width     3.1        2.7       3.6 #>   Petal.Width     0.1        1.4       2.5 #>   Petal.Length    1.5        3.9       6.1 #>  #> , , obs = 11 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.4        5.0       6.5 #>   Sepal.Width     3.7        2.0       3.2 #>   Petal.Width     0.2        1.0       2.0 #>   Petal.Length    1.5        3.5       5.1 #>  #> , , obs = 12 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.8        5.9       6.4 #>   Sepal.Width     3.4        3.0       2.7 #>   Petal.Width     0.2        1.5       1.9 #>   Petal.Length    1.6        4.2       5.3 #>  #> , , obs = 13 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.8        6.0       6.8 #>   Sepal.Width     3.0        2.2       3.0 #>   Petal.Width     0.1        1.0       2.1 #>   Petal.Length    1.4        4.0       5.5 #>  #> , , obs = 14 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.3        6.1       5.7 #>   Sepal.Width     3.0        2.9       2.5 #>   Petal.Width     0.1        1.4       2.0 #>   Petal.Length    1.1        4.7       5.0 #>  #> , , obs = 15 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.8        5.6       5.8 #>   Sepal.Width     4.0        2.9       2.8 #>   Petal.Width     0.2        1.3       2.4 #>   Petal.Length    1.2        3.6       5.1 #>  #> , , obs = 16 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.7        6.7       6.4 #>   Sepal.Width     4.4        3.1       3.2 #>   Petal.Width     0.4        1.4       2.3 #>   Petal.Length    1.5        4.4       5.3 #>  #> , , obs = 17 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.4        5.6       6.5 #>   Sepal.Width     3.9        3.0       3.0 #>   Petal.Width     0.4        1.5       1.8 #>   Petal.Length    1.3        4.5       5.5 #>  #> , , obs = 18 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        5.8       7.7 #>   Sepal.Width     3.5        2.7       3.8 #>   Petal.Width     0.3        1.0       2.2 #>   Petal.Length    1.4        4.1       6.7 #>  #> , , obs = 19 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.7        6.2       7.7 #>   Sepal.Width     3.8        2.2       2.6 #>   Petal.Width     0.3        1.5       2.3 #>   Petal.Length    1.7        4.5       6.9 #>  #> , , obs = 20 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        5.6       6.0 #>   Sepal.Width     3.8        2.5       2.2 #>   Petal.Width     0.3        1.1       1.5 #>   Petal.Length    1.5        3.9       5.0 #>  #> , , obs = 21 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.4        5.9       6.9 #>   Sepal.Width     3.4        3.2       3.2 #>   Petal.Width     0.2        1.8       2.3 #>   Petal.Length    1.7        4.8       5.7 #>  #> , , obs = 22 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        6.1       5.6 #>   Sepal.Width     3.7        2.8       2.8 #>   Petal.Width     0.4        1.3       2.0 #>   Petal.Length    1.5        4.0       4.9 #>  #> , , obs = 23 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.6        6.3       7.7 #>   Sepal.Width     3.6        2.5       2.8 #>   Petal.Width     0.2        1.5       2.0 #>   Petal.Length    1.0        4.9       6.7 #>  #> , , obs = 24 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        6.1       6.3 #>   Sepal.Width     3.3        2.8       2.7 #>   Petal.Width     0.5        1.2       1.8 #>   Petal.Length    1.7        4.7       4.9 #>  #> , , obs = 25 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.8        6.4       6.7 #>   Sepal.Width     3.4        2.9       3.3 #>   Petal.Width     0.2        1.3       2.1 #>   Petal.Length    1.9        4.3       5.7 #>  #> , , obs = 26 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        6.6       7.2 #>   Sepal.Width     3.0        3.0       3.2 #>   Petal.Width     0.2        1.4       1.8 #>   Petal.Length    1.6        4.4       6.0 #>  #> , , obs = 27 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        6.8       6.2 #>   Sepal.Width     3.4        2.8       2.8 #>   Petal.Width     0.4        1.4       1.8 #>   Petal.Length    1.6        4.8       4.8 #>  #> , , obs = 28 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.2        6.7       6.1 #>   Sepal.Width     3.5        3.0       3.0 #>   Petal.Width     0.2        1.7       1.8 #>   Petal.Length    1.5        5.0       4.9 #>  #> , , obs = 29 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.2        6.0       6.4 #>   Sepal.Width     3.4        2.9       2.8 #>   Petal.Width     0.2        1.5       2.1 #>   Petal.Length    1.4        4.5       5.6 #>  #> , , obs = 30 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.7        5.7       7.2 #>   Sepal.Width     3.2        2.6       3.0 #>   Petal.Width     0.2        1.0       1.6 #>   Petal.Length    1.6        3.5       5.8 #>  #> , , obs = 31 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.8        5.5       7.4 #>   Sepal.Width     3.1        2.4       2.8 #>   Petal.Width     0.2        1.1       1.9 #>   Petal.Length    1.6        3.8       6.1 #>  #> , , obs = 32 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.4        5.5       7.9 #>   Sepal.Width     3.4        2.4       3.8 #>   Petal.Width     0.4        1.0       2.0 #>   Petal.Length    1.5        3.7       6.4 #>  #> , , obs = 33 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.2        5.8       6.4 #>   Sepal.Width     4.1        2.7       2.8 #>   Petal.Width     0.1        1.2       2.2 #>   Petal.Length    1.5        3.9       5.6 #>  #> , , obs = 34 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.5        6.0       6.3 #>   Sepal.Width     4.2        2.7       2.8 #>   Petal.Width     0.2        1.6       1.5 #>   Petal.Length    1.4        5.1       5.1 #>  #> , , obs = 35 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.9        5.4       6.1 #>   Sepal.Width     3.1        3.0       2.6 #>   Petal.Width     0.2        1.5       1.4 #>   Petal.Length    1.5        4.5       5.6 #>  #> , , obs = 36 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        6.0       7.7 #>   Sepal.Width     3.2        3.4       3.0 #>   Petal.Width     0.2        1.6       2.3 #>   Petal.Length    1.2        4.5       6.1 #>  #> , , obs = 37 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.5        6.7       6.3 #>   Sepal.Width     3.5        3.1       3.4 #>   Petal.Width     0.2        1.5       2.4 #>   Petal.Length    1.3        4.7       5.6 #>  #> , , obs = 38 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.9        6.3       6.4 #>   Sepal.Width     3.6        2.3       3.1 #>   Petal.Width     0.1        1.3       1.8 #>   Petal.Length    1.4        4.4       5.5 #>  #> , , obs = 39 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.4        5.6       6.0 #>   Sepal.Width     3.0        3.0       3.0 #>   Petal.Width     0.2        1.3       1.8 #>   Petal.Length    1.3        4.1       4.8 #>  #> , , obs = 40 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        5.5       6.9 #>   Sepal.Width     3.4        2.5       3.1 #>   Petal.Width     0.2        1.3       2.1 #>   Petal.Length    1.5        4.0       5.4 #>  #> , , obs = 41 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        5.5       6.7 #>   Sepal.Width     3.5        2.6       3.1 #>   Petal.Width     0.3        1.2       2.4 #>   Petal.Length    1.3        4.4       5.6 #>  #> , , obs = 42 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.5        6.1       6.9 #>   Sepal.Width     2.3        3.0       3.1 #>   Petal.Width     0.3        1.4       2.3 #>   Petal.Length    1.3        4.6       5.1 #>  #> , , obs = 43 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.4        5.8       5.8 #>   Sepal.Width     3.2        2.6       2.7 #>   Petal.Width     0.2        1.2       1.9 #>   Petal.Length    1.3        4.0       5.1 #>  #> , , obs = 44 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        5.0       6.8 #>   Sepal.Width     3.5        2.3       3.2 #>   Petal.Width     0.6        1.0       2.3 #>   Petal.Length    1.6        3.3       5.9 #>  #> , , obs = 45 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        5.6       6.7 #>   Sepal.Width     3.8        2.7       3.3 #>   Petal.Width     0.4        1.3       2.5 #>   Petal.Length    1.9        4.2       5.7 #>  #> , , obs = 46 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.8        5.7       6.7 #>   Sepal.Width     3.0        3.0       3.0 #>   Petal.Width     0.3        1.2       2.3 #>   Petal.Length    1.4        4.2       5.2 #>  #> , , obs = 47 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        5.7       6.3 #>   Sepal.Width     3.8        2.9       2.5 #>   Petal.Width     0.2        1.3       1.9 #>   Petal.Length    1.6        4.2       5.0 #>  #> , , obs = 48 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.6        6.2       6.5 #>   Sepal.Width     3.2        2.9       3.0 #>   Petal.Width     0.2        1.3       2.0 #>   Petal.Length    1.4        4.3       5.2 #>  #> , , obs = 49 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.3        5.1       6.2 #>   Sepal.Width     3.7        2.5       3.4 #>   Petal.Width     0.2        1.1       2.3 #>   Petal.Length    1.5        3.0       5.4 #>  #> , , obs = 50 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        5.7       5.9 #>   Sepal.Width     3.3        2.8       3.0 #>   Petal.Width     0.2        1.3       1.8 #>   Petal.Length    1.4        4.1       5.1 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate negative log-likelihood — .nll","title":"Calculate negative log-likelihood — .nll","text":"llik(), except negated requiring lambda log scale (used combination nlm(), ensure optimization search stray negative values lambda).","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate negative log-likelihood — .nll","text":"","code":".nll(log.lambda, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate negative log-likelihood — .nll","text":"log.lambda natural logarithm incidence rate ... Arguments passed llik pop_data data.frame() cross-sectional serology data per antibody age, additional columns antigen_isos Character vector listing one antigen isotypes. Values must match pop_data. curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay noise_params data.frame() (tibble::tibble()) containing following variables, specifying noise parameters antigen isotype: antigen_iso: antigen isotype whose noise parameters specified row nu: biological noise eps: measurement noise y.low: lower limit detection current antigen isotype y.high: upper limit detection current antigen isotype verbose logical: TRUE, print verbose log information console","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate negative log-likelihood — .nll","text":"negative log-likelihood data current parameter values","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Seroincidence — est.incidence.by","title":"Estimate Seroincidence — est.incidence.by","text":"Function estimate seroincidences based cross-section serology data longitudinal response model.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Seroincidence — est.incidence.by","text":"","code":"est.incidence.by(   pop_data,   curve_params,   noise_params,   strata,   curve_strata_varnames = strata,   noise_strata_varnames = strata,   antigen_isos = pop_data %>% pull(\"antigen_iso\") %>% unique(),   lambda_start = 0.1,   build_graph = FALSE,   num_cores = 1L,   verbose = FALSE,   print_graph = FALSE,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Seroincidence — est.incidence.by","text":"pop_data data.frame() cross-sectional serology data per antibody age, additional columns identify possible strata. curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay noise_params data.frame() (tibble::tibble()) containing following variables, specifying noise parameters antigen isotype: antigen_iso: antigen isotype whose noise parameters specified row nu: biological noise eps: measurement noise y.low: lower limit detection current antigen isotype y.high: upper limit detection current antigen isotype strata Character vector stratum-defining variables. Values must variable names pop_data. curve_strata_varnames subset strata. Values must variable names curve_params. Default = \"\". noise_strata_varnames subset strata. Values must variable names noise_params. Default = \"\". antigen_isos Character vector one antibody names. Values must match pop_data lambda_start starting guess incidence rate, years/event. build_graph whether graph log-likelihood function across range incidence rates (lambda values) num_cores Number processor cores use calculations computing strata. set 1 package parallel available, computations executed parallel. Default = 1L. verbose logical: TRUE, print verbose log information console print_graph whether display log-likelihood curve graph course running est.incidence() ... Arguments passed est.incidence, stats::nlm stepmin positive scalar providing minimum allowable relative step length. stepmax positive scalar gives maximum allowable     scaled step length.  stepmax used prevent steps     cause optimization function overflow, prevent     algorithm leaving area interest parameter space,     detect divergence algorithm. stepmax chosen     small enough prevent first two occurrences,     larger anticipated reasonable step. typsize estimate size parameter     minimum. fscale estimate size f minimum. ndigit number significant digits function f. gradtol positive scalar giving tolerance     scaled gradient considered close enough zero     terminate algorithm.  scaled gradient     measure relative change f direction     p[] divided relative change p[]. iterlim positive integer specifying maximum number     iterations performed program terminated. check.analyticals logical scalar specifying whether     analytic gradients Hessians, supplied,     checked numerical derivatives initial parameter     values. can help detect incorrectly formulated gradients     Hessians.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Seroincidence — est.incidence.by","text":"strata meaningful inputs: object class \"seroincidence.\"; .e., list \"seroincidence\" objects est.incidence(), one stratum, meta-data attributes. strata missing, NULL, NA, \"\": object class \"seroincidence\".","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.by.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Seroincidence — est.incidence.by","text":"strata left empty, warning produced, recommending use est.incidence() unstratified analyses, data passed est.incidence(). reason want use est.incidence.() strata instead calling est.incidence(), may use NA, NULL, \"\" strata argument avoid warning.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Seroincidence — est.incidence.by","text":"","code":"library(dplyr)  xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") %>%   clean_pop_data() curve = load_curve_params(\"https://osf.io/download/rtw5k/\") noise = load_noise_params(\"https://osf.io/download//hqy4v/\")  est2 = est.incidence.by( strata = c(\"catchment\"), pop_data = xs_data %>% filter(Country == \"Pakistan\"), curve_params = curve, noise_params = noise %>% filter(Country == \"Pakistan\"), antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"), #num_cores = 8 #Allow for parallel processing to decrease run time ) #> data format is as expected. #> Warning: curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning: noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> data format is as expected. #> data format is as expected.  summary(est2) #> Seroincidence estimated given the following setup: #> a) Antigen isotypes   : HlyE_IgG, HlyE_IgA  #> b) Strata       : catchment  #>  #>  Seroincidence estimates: #> # A tibble: 2 × 13 #>   Stratum   catchment     n est.start incidence.rate      SE CI.lwr CI.upr #>   <chr>     <chr>     <int>     <dbl>          <dbl>   <dbl>  <dbl>  <dbl> #> 1 Stratum 1 aku         294       0.1          0.106 0.00767 0.0916  0.122 #> 2 Stratum 2 kgh         200       0.1          0.167 0.0133  0.143   0.196 #> # ℹ 5 more variables: coverage <dbl>, log.lik <dbl>, iterations <int>, #> #   antigen.isos <chr>, nlm.convergence.code <ord>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the maximum likelihood estimate of the incidence rate parameter — est.incidence","title":"Find the maximum likelihood estimate of the incidence rate parameter — est.incidence","text":"function models seroincidence using maximum likelihood estimation; , finds value seroincidence parameter maximizes likelihood (.e., joint probability) data.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the maximum likelihood estimate of the incidence rate parameter — est.incidence","text":"","code":"est.incidence(   pop_data,   curve_params,   noise_params,   antigen_isos = pop_data$antigen_iso %>% unique(),   lambda_start = 0.1,   stepmin = 1e-08,   stepmax = 3,   verbose = FALSE,   build_graph = FALSE,   print_graph = build_graph & verbose,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the maximum likelihood estimate of the incidence rate parameter — est.incidence","text":"pop_data data.frame() cross-sectional serology data per antibody age, additional columns curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay noise_params data.frame() (tibble::tibble()) containing following variables, specifying noise parameters antigen isotype: antigen_iso: antigen isotype whose noise parameters specified row nu: biological noise eps: measurement noise y.low: lower limit detection current antigen isotype y.high: upper limit detection current antigen isotype antigen_isos Character vector one antibody names. Values must match pop_data lambda_start starting guess incidence rate, years/event. stepmin positive scalar providing minimum allowable relative step length. stepmax positive scalar gives maximum allowable     scaled step length.  stepmax used prevent steps     cause optimization function overflow, prevent     algorithm leaving area interest parameter space,     detect divergence algorithm. stepmax chosen     small enough prevent first two occurrences,     larger anticipated reasonable step. verbose logical: TRUE, print verbose log information console build_graph whether graph log-likelihood function across range incidence rates (lambda values) print_graph whether display log-likelihood curve graph course running est.incidence() ... Arguments passed stats::nlm typsize estimate size parameter     minimum. fscale estimate size f minimum. ndigit number significant digits function f. gradtol positive scalar giving tolerance     scaled gradient considered close enough zero     terminate algorithm.  scaled gradient     measure relative change f direction     p[] divided relative change p[]. iterlim positive integer specifying maximum number     iterations performed program terminated. check.analyticals logical scalar specifying whether     analytic gradients Hessians, supplied,     checked numerical derivatives initial parameter     values. can help detect incorrectly formulated gradients     Hessians.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the maximum likelihood estimate of the incidence rate parameter — est.incidence","text":"\"seroincidence\" object, stats::nlm() fit object extra meta-data attributes lambda_start, antigen_isos, ll_graph","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find the maximum likelihood estimate of the incidence rate parameter — est.incidence","text":"","code":"library(dplyr)  xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") %>% clean_pop_data() curve = load_curve_params(\"https://osf.io/download/rtw5k/\") noise = load_noise_params(\"https://osf.io/download//hqy4v/\")  est1 = est.incidence( pop_data = xs_data %>% filter(Country == \"Pakistan\"), curve_param = curve, noise_param = noise %>% filter(Country == \"Pakistan\"), antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\") ) #> data format is as expected.  summary(est1) #> # A tibble: 1 × 10 #>   est.start incidence.rate      SE CI.lwr CI.upr coverage log.lik iterations #>       <dbl>          <dbl>   <dbl>  <dbl>  <dbl>    <dbl>   <dbl>      <int> #> 1       0.1          0.128 0.00682  0.115  0.142     0.95  -2376.          4 #> # ℹ 2 more variables: antigen.isos <chr>, nlm.convergence.code <ord>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/fdev.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate negative log-likelihood (deviance) — fdev","title":"Calculate negative log-likelihood (deviance) — fdev","text":"description added ","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/fdev.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate negative log-likelihood (deviance) — fdev","text":"","code":"fdev(lambda, csdata, lnpars, cond)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/fdev.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate negative log-likelihood (deviance) — fdev","text":"lambda incidence parameter, events per person-year csdata cross-sectional sample data containing variables value age lnpars longitudinal antibody decay model parameters alpha, y1, d cond measurement noise parameters nu, eps, y.low, y.high","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Additional Data — getAdditionalData","title":"Get Additional Data — getAdditionalData","text":"Retrieves additional data internet. data format must .RDS zipped .RDS. purpose function download data longitudinal response parameters online repository cross-sectional population data. Data package available : https://osf.io/ne8pc/files/osfstorage can save data chosen directory using optional savePath argument. Specify file path file name. Large datasets may timeout. , can increase download time updating maximum timeout time code . (Ex: increase 300 1000) options(timeout = max(300, getOption(\"timeout\")))","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Additional Data — getAdditionalData","text":"","code":"getAdditionalData(fileURL, savePath = NULL)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Additional Data — getAdditionalData","text":"fileURL URL file downloaded. savePath Folder directory filename save downloaded unzipped (needed) file. File saved argument NULL. Optional. Default = NULL.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Additional Data — getAdditionalData","text":"R object stored file indicated fileURL input Data object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Additional Data — getAdditionalData","text":"","code":"if (FALSE) { curve_param_samples =   getAdditionalData(     fileURL = \"https://osf.io/download/bhfvx\")  # optionally, save the data to disk curve_param_samples =   getAdditionalData(     fileURL = \"https://osf.io/download/bhfvx\",     savePath = \"~/Downloads/curv_params.rds\") }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.curve.params.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph estimated antibody decay curve — graph.curve.params","title":"Graph estimated antibody decay curve — graph.curve.params","text":"Graph estimated antibody decay curve","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.curve.params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph estimated antibody decay curve — graph.curve.params","text":"","code":"graph.curve.params(   curve_params,   antigen_isos = unique(curve_params$antigen_iso),   verbose = FALSE )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.curve.params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph estimated antibody decay curve — graph.curve.params","text":"curve_params data.frame() containing MCMC samples antibody decay curve parameters antigen_isos antigen isotypes verbose verbose output","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.curve.params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph estimated antibody decay curve — graph.curve.params","text":"ggplot2::ggplot() object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.curve.params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph estimated antibody decay curve — graph.curve.params","text":"","code":"if (FALSE) { curve_params = readRDS(url(\"https://osf.io/download/rtw5k/\")) plot1 = graph.curve.params(curve_params) print(plot1) }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.loglik.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph log-likelihood of data — graph.loglik","title":"Graph log-likelihood of data — graph.loglik","text":"Graph log-likelihood data","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.loglik.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph log-likelihood of data — graph.loglik","text":"","code":"graph.loglik(   pop_data,   curve_params,   noise_params,   antigen_isos,   x = 10^seq(-3, 0, by = 0.1),   highlight_points = NULL,   highlight_point_names = \"highlight_points\",   log_x = FALSE,   previous_plot = NULL,   curve_label = paste(antigen_isos, collapse = \" + \"),   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.loglik.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph log-likelihood of data — graph.loglik","text":"pop_data data.frame() cross-sectional serology data per antibody age, additional columns curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay noise_params data.frame() (tibble::tibble()) containing following variables, specifying noise parameters antigen isotype: antigen_iso: antigen isotype whose noise parameters specified row nu: biological noise eps: measurement noise y.low: lower limit detection current antigen isotype y.high: upper limit detection current antigen isotype antigen_isos Character vector listing one antigen isotypes. Values must match pop_data. x sequence lambda values graph highlight_points possible highlighted value highlight_point_names labels highlighted points log_x x-axis logarithmic scale (TRUE) linear scale (FALSE, default)? previous_plot NULL, current data added existing graph curve_label NULL, add label curve ... Arguments passed llik verbose logical: TRUE, print verbose log information console","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.loglik.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph log-likelihood of data — graph.loglik","text":"ggplot2::ggplot()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.loglik.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph log-likelihood of data — graph.loglik","text":"","code":"library(dplyr) library(tibble)  #Load cross-sectional data xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") %>% clean_pop_data()   #Load curve parameters dmcmc = load_curve_params(\"https://osf.io/download/rtw5k\" )  #Load noise parameters cond <- tibble( antigen_iso = c(\"HlyE_IgG\", \"HlyE_IgA\"), nu = c(0.5, 0.5),                          # Biologic noise (nu) eps = c(0, 0),                             # M noise (eps) y.low = c(1, 1),                           # low cutoff (llod) y.high = c(5e6, 5e6))                      # high cutoff (y.high)  #Graph the log likelihood lik_HlyE_IgA = graph.loglik(  pop_data = xs_data,  curve_params = dmcmc,  noise_params = cond,  antigen_isos = \"HlyE_IgA\",  log_x = TRUE )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/ldpar.html","id":null,"dir":"Reference","previous_headings":"","what":"extract a row from longitudinal parameter set — ldpar","title":"extract a row from longitudinal parameter set — ldpar","text":"take random sample longitudinal parameter set given age infection,  list antibodies","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/ldpar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"extract a row from longitudinal parameter set — ldpar","text":"","code":"ldpar(age, antigen_isos, nmc, npar, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/ldpar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"extract a row from longitudinal parameter set — ldpar","text":"age age infection antigen_isos antigen isotypes nmc mcmc sample use npar number parameters ... passed simpar()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/ldpar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"extract a row from longitudinal parameter set — ldpar","text":"array parameters: c(y0,b0,mu0,mu1,c1,alpha,shape)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/llik.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate log-likelihood — llik","title":"Calculate log-likelihood — llik","text":"Calculates log-likelihood set cross-sectional antibody response data, given incidence rate (lambda) value.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/llik.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate log-likelihood — llik","text":"","code":"llik(   lambda,   pop_data,   antigen_isos,   curve_params,   noise_params,   verbose = FALSE,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/llik.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate log-likelihood — llik","text":"lambda incidence parameter, events per person-year pop_data data.frame() cross-sectional serology data per antibody age, additional columns antigen_isos Character vector listing one antigen isotypes. Values must match pop_data. curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay noise_params data.frame() (tibble::tibble()) containing following variables, specifying noise parameters antigen isotype: antigen_iso: antigen isotype whose noise parameters specified row nu: biological noise eps: measurement noise y.low: lower limit detection current antigen isotype y.high: upper limit detection current antigen isotype verbose logical: TRUE, print verbose log information console ... additional arguments passed functions (currently used).","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/llik.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate log-likelihood — llik","text":"log-likelihood data current parameter values","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/llik.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate log-likelihood — llik","text":"","code":"library(dplyr) library(tibble)  #load in longitudinal parameters dmcmc = load_curve_params(\"https://osf.io/download/rtw5k\")  xs_data <- \"https://osf.io/download//n6cp3/\" %>% load_pop_data() %>% clean_pop_data()  #Load noise params   cond <- tibble(   antigen_iso = c(\"HlyE_IgG\", \"HlyE_IgA\"),   nu = c(0.5, 0.5),                          # Biologic noise (nu)   eps = c(0, 0),                             # M noise (eps)   y.low = c(1, 1),                           # low cutoff (llod)   y.high = c(5e6, 5e6))                      # high cutoff (y.high)  #Calculate log-likelihood   ll_AG = llik(   pop_data = xs_data,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = c(\"HlyE_IgG\",\"HlyE_IgA\"),   lambda = 0.1) %>% print() #> [1] -9268.824"},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_curve_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Load antibody decay curve parameter samples — load_curve_params","title":"Load antibody decay curve parameter samples — load_curve_params","text":"Load antibody decay curve parameter samples","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_curve_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load antibody decay curve parameter samples — load_curve_params","text":"","code":"load_curve_params(file_path, antigen_isos = NULL)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_curve_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load antibody decay curve parameter samples — load_curve_params","text":"file_path path RDS file containing MCMC samples antibody decay curve parameters y0, y1, t1, alpha, r, stored data.frame() tibble::tbl_df antigen_isos character() vector antigen isotypes used analyses","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_curve_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load antibody decay curve parameter samples — load_curve_params","text":"curve_params object (tibble::tbl_df extra attribute antigen_isos)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_curve_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load antibody decay curve parameter samples — load_curve_params","text":"","code":"curve = load_curve_params(\"https://osf.io/download/rtw5k/\" ) print(curve) #> # A tibble: 20,000 × 7 #>    antigen_iso  iter     y0     y1    t1     alpha     r #>    <fct>       <int>  <dbl>  <dbl> <dbl>     <dbl> <dbl> #>  1 HlyE_IgA        1  2.48    63.5  9.52 0.000581   1.75 #>  2 HlyE_IgG        1  3.04   164.   6.55 0.00457    1.17 #>  3 LPS_IgA         1  0.748  103.   4.98 0.00308    1.58 #>  4 LPS_IgG         1  0.941  320.   6.14 0.00166    1.41 #>  5 Vi_IgG          1  8.46  4348.   3.07 0.0000340  1.06 #>  6 HlyE_IgA        2  3.86   288.   1.27 0.000459   2.66 #>  7 HlyE_IgG        2  1.82   154.  10.8  0.000921   1.30 #>  8 LPS_IgA         2  1.76   852.   2.49 0.000126   2.91 #>  9 LPS_IgG         2  0.434   20.6  4.29 0.00122    1.37 #> 10 Vi_IgG          2 18.8    345.   3.48 0.000142   1.02 #> # ℹ 19,990 more rows"},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_noise_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Load noise parameters — load_noise_params","title":"Load noise parameters — load_noise_params","text":"Load noise parameters","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_noise_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load noise parameters — load_noise_params","text":"","code":"load_noise_params(file_path, antigen_isos = NULL)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_noise_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load noise parameters — load_noise_params","text":"file_path path RDS file containing biologic measurement noise antibody decay curve parameters y.low, eps, nu, y.high, stored data.frame() tibble::tbl_df antigen_isos character() vector antigen isotypes used analyses","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_noise_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load noise parameters — load_noise_params","text":"noise object (tibble::tbl_df extra attribute antigen_isos)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_noise_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load noise parameters — load_noise_params","text":"","code":"noise = load_noise_params(\"https://osf.io/download//hqy4v/\") print(noise) #> # A tibble: 16 × 6 #>    antigen_iso Country    y.low   eps    nu  y.high #>    <chr>       <fct>      <dbl> <dbl> <dbl>   <dbl> #>  1 HlyE_IgA    Bangladesh 0.376 0.280  2.60 5000000 #>  2 HlyE_IgA    Ghana      0.179 0.240  2.60 5000000 #>  3 HlyE_IgA    Nepal      0.853 0.238  2.60 5000000 #>  4 HlyE_IgA    Pakistan   0.508 0.279  2.60 5000000 #>  5 HlyE_IgG    Bangladesh 0.787 0.306  2.36 5000000 #>  6 HlyE_IgG    Ghana      0.645 0.164  2.36 5000000 #>  7 HlyE_IgG    Nepal      1.89  0.128  2.36 5000000 #>  8 HlyE_IgG    Pakistan   1.59  0.146  2.36 5000000 #>  9 LPS_IgA     Bangladesh 0.660 0.299  2.14 5000000 #> 10 LPS_IgA     Ghana      0.861 0.163  2.14 5000000 #> 11 LPS_IgA     Nepal      1.79  0.115  2.14 5000000 #> 12 LPS_IgA     Pakistan   5.13  0.246  2.14 5000000 #> 13 LPS_IgG     Bangladesh 0.992 0.298  3.24 5000000 #> 14 LPS_IgG     Ghana      0.885 0.195  3.24 5000000 #> 15 LPS_IgG     Nepal      0.647 0.179  3.24 5000000 #> 16 LPS_IgG     Pakistan   4.84  0.273  3.24 5000000"},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_pop_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Load a cross-sectional antibody survey data set — load_pop_data","title":"Load a cross-sectional antibody survey data set — load_pop_data","text":"Load cross-sectional antibody survey data set","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_pop_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load a cross-sectional antibody survey data set — load_pop_data","text":"","code":"load_pop_data(file_path, antigen_isos = NULL)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_pop_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load a cross-sectional antibody survey data set — load_pop_data","text":"file_path path RDS file containing cross-sectional antibody survey data set, stored data.frame() tibble::tbl_df antigen_isos character() vector antigen isotypes used analyses","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_pop_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load a cross-sectional antibody survey data set — load_pop_data","text":"pop_data object (tibble::tbl_df extra attribute antigen_isos)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_pop_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load a cross-sectional antibody survey data set — load_pop_data","text":"","code":"xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") print(xs_data) #> # A tibble: 3,336 × 8 #>    index_id Country    cluster  catchment   Age ageCat antigen_iso result #>    <chr>    <chr>      <fct>    <chr>     <dbl> <fct>  <fct>        <dbl> #>  1 B1       Bangladesh Ward 2 N dhaka       4   <5     HlyE_IgA     7.24  #>  2 B2       Bangladesh Ward 2 N dhaka       4   <5     HlyE_IgG    24.0   #>  3 B3       Bangladesh Ward 2 N dhaka       3.7 <5     HlyE_IgA     0.836 #>  4 B4       Bangladesh Ward 2 N dhaka       3.7 <5     HlyE_IgG     3.17  #>  5 B5       Bangladesh Ward 2 N dhaka      10.6 5-15   HlyE_IgA     3.42  #>  6 B6       Bangladesh Ward 2 N dhaka      10.6 5-15   HlyE_IgG    14.3   #>  7 B7       Bangladesh Ward 2 N dhaka      16   16+    HlyE_IgA     8.84  #>  8 B8       Bangladesh Ward 2 N dhaka      16   16+    HlyE_IgG    14.2   #>  9 B9       Bangladesh Ward 2 N dhaka      16.5 16+    HlyE_IgA    10.3   #> 10 B10      Bangladesh Ward 2 N dhaka      16.5 16+    HlyE_IgG    29.4   #> # ℹ 3,326 more rows"},{"path":"https://ucd-serg.github.io/serocalculator/reference/mkbaseline.html","id":null,"dir":"Reference","previous_headings":"","what":"generate random sample from baseline distribution — mkbaseline","title":"generate random sample from baseline distribution — mkbaseline","text":"generate random sample baseline distribution","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/mkbaseline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generate random sample from baseline distribution — mkbaseline","text":"","code":"mkbaseline(kab, n = 1, blims, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/mkbaseline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generate random sample from baseline distribution — mkbaseline","text":"kab index row antibody baseline limits read blims n number observations blims range possible baseline antibody levels ... currently used","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/mkbaseline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"generate random sample from baseline distribution — mkbaseline","text":"numeric() vector","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/plot_curve_params_one_ab.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph an antibody decay curve model — plot_curve_params_one_ab","title":"Graph an antibody decay curve model — plot_curve_params_one_ab","text":"Graph antibody decay curve model","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/plot_curve_params_one_ab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph an antibody decay curve model — plot_curve_params_one_ab","text":"","code":"plot_curve_params_one_ab(   object,   verbose = FALSE,   alpha = 0.4,   n_curves = 100,   log_x = FALSE,   rows_to_graph = 1:min(n_curves, nrow(object)),   xlim = c(10^-1, 10^3.1),   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/plot_curve_params_one_ab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph an antibody decay curve model — plot_curve_params_one_ab","text":"object data.frame() curve parameters (one MCMC samples) verbose verbose output alpha (passed ggplot2::geom_function()) transparent curves : 0 = fully transparent (invisible) 1 = fully opaque n_curves many curves plot (see details). log_x x-axis logarithmic scale (TRUE) linear scale (FALSE, default)? rows_to_graph rows curve_params plot (overrides n_curves). xlim range x values graph ... arguments passed layer(). often aesthetics, used set aesthetic fixed value, like colour = \"red\" size = 3. may also parameters paired geom/stat.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/plot_curve_params_one_ab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph an antibody decay curve model — plot_curve_params_one_ab","text":"ggplot2::ggplot() object","code":""},{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/reference/plot_curve_params_one_ab.html","id":"n-curves-and-rows-to-graph","dir":"Reference","previous_headings":"","what":"n_curves and rows_to_graph","title":"Graph an antibody decay curve model — plot_curve_params_one_ab","text":"cases, curve_params contain many rows MCMC samples samples plotted . Setting  n_curves argument value smaller number rows curve_params cause function select first n_curves rows graph. Setting n_curves larger number rows ` result curves plotted. user directly specifies rows_to_graph argument, n_curves effect.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/plot_curve_params_one_ab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph an antibody decay curve model — plot_curve_params_one_ab","text":"","code":"library(dplyr) # loads the `%>%` operator and `dplyr::filter()` \"https://osf.io/download/rtw5k/\" %>% load_curve_params() %>% dplyr::filter(antigen_iso == \"HlyE_IgG\") %>% serocalculator:::plot_curve_params_one_ab()"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Method for seroincidence.by Object — print.seroincidence.by","title":"Print Method for seroincidence.by Object — print.seroincidence.by","text":"Custom print() function show output seroincidence calculator est.incidence.().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Method for seroincidence.by Object — print.seroincidence.by","text":"","code":"# S3 method for seroincidence.by print(x, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Method for seroincidence.by Object — print.seroincidence.by","text":"x list containing output function est.incidence.(). ... Additional arguments affecting summary produced.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Method for seroincidence.by Object — print.seroincidence.by","text":"","code":"if (FALSE) { # estimate seroincidence seroincidence <- est.incidence.by(...)  # print the seroincidence object to the console print(seroincidence)  # or simply type (appropriate print method will be invoked automatically) seroincidence }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Method for seroincidence Object — print.seroincidence","title":"Print Method for seroincidence Object — print.seroincidence","text":"Custom print() function show output seroincidence calculator est.incidence().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Method for seroincidence Object — print.seroincidence","text":"","code":"# S3 method for seroincidence print(x, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Method for seroincidence Object — print.seroincidence","text":"x list containing output function est.incidence.(). ... Additional arguments affecting summary produced.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Method for seroincidence Object — print.seroincidence","text":"","code":"if (FALSE) { # estimate seroincidence seroincidence <- est.incidence.by(...)  # print the seroincidence object to the console print(seroincidence)  # or simply type (appropriate print method will be invoked automatically) seroincidence }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Method for Seroincidence Summary Object — print.summary.seroincidence.by","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence.by","text":"Custom print() function \"summary.seroincidence.\" objects (constructed summary.seroincidence.())","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence.by","text":"","code":"# S3 method for summary.seroincidence.by print(x, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence.by","text":"x \"summary.seroincidence.\" object (constructed summary.seroincidence.()) ... Additional arguments affecting summary produced.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence.by","text":"","code":"if (FALSE) { # estimate seroincidence seroincidence <- est.incidence.by(...)  # calculate summary statistics for the seroincidence object seroincidenceSummary <- summary(seroincidence)  # print the summary of seroincidence object to the console print(seroincidenceSummary)  # or simply type (appropriate print method will be invoked automatically) seroincidenceSummary }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimating Infection Rates from Serological Data — serocalculator","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"package translates antibody levels measured (cross-sectional) population sample estimate frequency seroconversions (infections) occur sampled population. API package includes following functions:","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"_PACKAGE","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"Methods estimating seroincidence Teunis, P. F. M., J. C. H. van Eijkeren. \"Estimation seroconversion rates infectious diseases: Effects age noise.\" Statistics Medicine 39, . 21 (2020): 2799-2814. Teunis, P. F. M., J. C. H. van Eijkeren, W. F. de Graaf, . Bonačić Marinović, M. E. E. Kretzschmar. \"Linking seroresponse infection within-host heterogeneity antibody production.\" Epidemics 16 (2016): 33-39. Applications Aiemjoy, Kristen, Jessica C. Seidman, Senjuti Saha, Sira Jam Munira, Mohammad Saiful Islam Sajib, Syed Muktadir Al Sium, Anik Sarkar et al. \"Estimating typhoid incidence community-based serosurveys: multicohort study.\" Lancet Microbe 3, . 8 (2022): e578-e587. Aiemjoy, Kristen, John Rumunu, Juma John Hassen, Kirsten E. Wiens, Denise Garrett, Polina Kamenskaya, Jason B. Harris et al. \"Seroincidence enteric fever, Juba, South Sudan.\" Emerging infectious diseases 28, . 11 (2022): 2316. Monge, S., Teunis, P. F., Friesema, ., Franz, E., Ang, W., van Pelt, W., Mughini-Gras, L. \"Immune response-eliciting exposure Campylobacter vastly exceeds incidence clinically overt campylobacteriosis associated similar risk factors: nationwide serosurvey Netherlands\" Journal Infection, 2018, 1--7, doi:10.1016/j.jinf.2018.04.016 Kretzschmar, M., Teunis, P. F., Pebody, R. G. \"Incidence reproduction numbers pertussis: estimates serological social contact data five European countries\" PLoS Medicine 7, . 6 (June 1, 2010):e1000291. doi:10.1371/journal.pmed.1000291. Simonsen, J., Strid, M. ., Molbak, K., Krogfelt, K. ., Linneberg, ., Teunis, P. \"Sero-epidemiology tool study incidence Salmonella infections humans\" Epidemiology Infection 136, . 7 (July 1, 2008): 895--902. doi:10.1017/S0950268807009314 Simonsen, J., Teunis, P. F., van Pelt, W., van Duynhoven, Y., Krogfelt, K. ., Sadkowska-Todys, M., Molbak K. \"Usefulness seroconversion rates comparing infection pressures countries\" Epidemiology Infection, April 12, 2010, 1--8. doi:10.1017/S0950268810000750. Falkenhorst, G., Simonsen, J., Ceper, T. H., van Pelt, W., de Valk, H., Sadkowska-Todys, M., Zota, L., Kuusi, M., Jernberg, C., Rota, M. C., van Duynhoven, Y. T., Teunis, P. F., Krogfelt, K. ., Molbak, K. \"Serological cross-sectional studies salmonella incidence eight European countries: correlation incidence reported cases\" BMC Public Health 12, . 1 (July 15, 2012): 523--23. doi:10.1186/1471-2458-12-523. Teunis, P. F., Falkenhorst, G., Ang, C. W., Strid, M. ., De Valk, H., Sadkowska-Todys, M., Zota, L., Kuusi, M., Rota, M. C., Simonsen, J. B., Molbak, K., Van Duynhoven, Y. T., van Pelt, W. \"Campylobacter seroconversion rates selected countries European Union\" Epidemiology Infection 141, . 10 (2013): 2051--57. doi:10.1017/S0950268812002774. de Melker, H. E., Versteegh, F. G., Schellekens, J. F., Teunis, P. F., Kretzschmar, M. \"incidence Bordetella pertussis infections estimated population combination serological surveys\" Journal Infection 53, . 2 (August 1, 2006): 106--13. doi:10.1016/j.jinf.2005.10.020 Quantification seroresponse de Graaf, W. F., Kretzschmar, M. E., Teunis, P. F., Diekmann, O. \"two-phase within-host model immune response application serological profiles pertussis\" Epidemics 9 (2014):1--7. doi:10.1016/j.epidem.2014.08.002. Berbers, G. ., van de Wetering, M. S., van Gageldonk, P. G., Schellekens, J. F., Versteegh, F. G., Teunis, P.F. \"novel method evaluating natural vaccine induced serological responses Bordetella pertussis antigens\" Vaccine 31, . 36 (August 12, 2013): 3732--38. doi:10.1016/j.vaccine.2013.05.073. Versteegh, F. G., Mertens, P. L., de Melker, H. E., Roord, J. J., Schellekens, J. F., Teunis, P. F. \"Age-specific long-term course IgG antibodies pertussis toxin symptomatic infection Bordetella pertussis\" Epidemiology Infection 133, . 4 (August 1, 2005): 737--48. Teunis, P. F., van der Heijden, O. G., de Melker, H. E., Schellekens, J. F., Versteegh, F. G., Kretzschmar, M. E. \"Kinetics IgG antibody response pertussis toxin infection B. pertussis\" Epidemiology Infection 129, . 3 (December 10, 2002):479. doi:10.1017/S0950268802007896.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"Peter Teunis p.teunis@emory.edu Doug Ezra Morrison demorrison@ucdavis.edu Kristen Aiemjoy kaiemjoy@ucdavis.edu Kristina Lai kwlai@ucdavis.edu","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate a cross-sectional serosurvey with noise — sim.cs","title":"Simulate a cross-sectional serosurvey with noise — sim.cs","text":"Makes cross-sectional data set (age, y(t) set) adds noise, desired.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate a cross-sectional serosurvey with noise — sim.cs","text":"","code":"sim.cs(   lambda = 0.1,   n.smpl = 100,   age.rng = c(0, 20),   age.fx = NA,   antigen_isos,   n.mc = 0,   renew.params = FALSE,   add.noise = FALSE,   curve_params,   noise_limits,   format = \"wide\",   verbose = FALSE,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate a cross-sectional serosurvey with noise — sim.cs","text":"lambda numeric() scalar indicating incidence rate (events per person-years) n.smpl number samples simulate age.rng age range sampled individuals, years age.fx specify curve parameters use age (nothing present?) antigen_isos Character vector one antibody names. Values must match curve_params. n.mc many MCMC samples use: n.mc 1:4000 fixed posterior sample used n.mc = 0, random sample chosen renew.params whether generate new parameter set infection renew.params = TRUE generates new parameter set infection renew.params = FALSE keeps one selected birth, updates baseline y0 add.noise logical() indicating whether add biological measurement noise curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay noise_limits biologic noise distribution parameters format character() variable, containing either: \"long\" (one measurement per row) \"wide\" (one serum sample per row) verbose logical: TRUE, print verbose log information console ... Arguments passed simcs.tinf","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate a cross-sectional serosurvey with noise — sim.cs","text":"tibble::tbl_df containing simulated cross-sectional serosurvey data, columns: age: age (days) one column element antigen_iso input argument","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate a cross-sectional serosurvey with noise — sim.cs","text":"","code":"#Load curve parameters dmcmc = load_curve_params(\"https://osf.io/download/rtw5k\")  #Specify the antibody-isotype responses to include in analyses antibodies = c(\"HlyE_IgA\", \"HlyE_IgG\")  #set seed to reproduce results set.seed(54321)  # simulated incidence rate per person-year lambda <- 0.2;  # range covered in simulations lifespan <- c(0, 10);  # cross-sectional sample size nrep <- 100  # biologic noise distribution dlims <- rbind(   \"HlyE_IgA\" = c(min = 0, max = 0.5),   \"HlyE_IgG\" = c(min = 0, max = 0.5))  #generate cross-sectional data csdata <- sim.cs(   curve_params = dmcmc,   lambda = lambda,   n.smpl = nrep,   age.rng = lifespan,   antigen_isos = antibodies,  n.mc = 0,  renew.params = TRUE,  add.noise = TRUE,  noise_limits = dlims,  format = \"long\" )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.multi.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate multiple data sets — sim.cs.multi","title":"Simulate multiple data sets — sim.cs.multi","text":"Simulate multiple data sets","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.multi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate multiple data sets — sim.cs.multi","text":"","code":"sim.cs.multi(   nclus = 10,   lambdas = c(0.05, 0.1, 0.15, 0.2, 0.3),   num_cores = max(1, parallel::detectCores() - 1),   rng_seed = 1234,   renew.params = TRUE,   add.noise = TRUE,   verbose = FALSE,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.multi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate multiple data sets — sim.cs.multi","text":"nclus number clusters lambdas #incidence rate, events/person*year num_cores number cores use parallel computations rng_seed starting seed random number generator, passed rngtools::RNGseq() renew.params whether generate new parameter set infection renew.params = TRUE generates new parameter set infection renew.params = FALSE keeps one selected birth, updates baseline y0 add.noise logical() indicating whether add biological measurement noise verbose whether report verbose information ... Arguments passed sim.cs lambda numeric() scalar indicating incidence rate (events per person-years) n.smpl number samples simulate age.rng age range sampled individuals, years age.fx specify curve parameters use age (nothing present?) antigen_isos Character vector one antibody names. Values must match curve_params. n.mc many MCMC samples use: n.mc 1:4000 fixed posterior sample used n.mc = 0, random sample chosen noise_limits biologic noise distribution parameters format character() variable, containing either: \"long\" (one measurement per row) \"wide\" (one serum sample per row) curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.multi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate multiple data sets — sim.cs.multi","text":"tibble::tibble()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/simcs.tinf.html","id":null,"dir":"Reference","previous_headings":"","what":"collect cross-sectional data — simcs.tinf","title":"collect cross-sectional data — simcs.tinf","text":"output: (age, y(t) set)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/simcs.tinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"collect cross-sectional data — simcs.tinf","text":"","code":"simcs.tinf(   lambda,   n.smpl,   age.rng,   age.fx = NA,   antigen_isos,   n.mc = 0,   renew.params = FALSE,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/simcs.tinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"collect cross-sectional data — simcs.tinf","text":"lambda seroconversion rate (events/person-day) n.smpl number samples n.smpl (= nr simulated records) age.rng age range use simulating data, days age.fx age.fx parameter sample (age.fx = NA age infection) antigen_isos Character vector one antibody names. Values must match curve_params. n.mc n.mc 1:4000 fixed posterior sample used n.mc = 0 random sample chosen renew.params renew.params = TRUE generates new parameter set infection renew.params = FALSE keeps one selected birth, updates baseline y0 ... arguments passed simresp.tinf()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/simcs.tinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"collect cross-sectional data — simcs.tinf","text":"array()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/simresp.tinf.html","id":null,"dir":"Reference","previous_headings":"","what":"simulate antibody kinetics of y over a time interval — simresp.tinf","title":"simulate antibody kinetics of y over a time interval — simresp.tinf","text":"simulate antibody kinetics y time interval","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/simresp.tinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"simulate antibody kinetics of y over a time interval — simresp.tinf","text":"","code":"simresp.tinf(   lambda,   t.end,   age.fx,   antigen_isos,   n.mc = 0,   renew.params,   predpar,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/simresp.tinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"simulate antibody kinetics of y over a time interval — simresp.tinf","text":"lambda seroconversion rate (1/days), t.end end time interval (beginning time 0) days(?) age.fx parameter estimates fixed age (age.fx years) . age.fx = NA age infection used. antigen_isos antigen isotypes n.mc posterior sample may selected (1:4000), n.mc = 0 posterior sample chosen random. renew.params infection, new parameter sample may generated (renew.params = TRUE). Otherwise (renew.params = FALSE), sample generated birth kept, baseline y0 carried prior infections. predpar array() dimensions named: antigen_iso parameter obs ... Arguments passed ldpar, ab, mkbaseline age age infection nmc mcmc sample use npar number parameters t age infection? par parameters kab index row antibody baseline limits read blims n number observations blims range possible baseline antibody levels","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/simresp.tinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"simulate antibody kinetics of y over a time interval — simresp.tinf","text":"function returns list() : t = times (days, birth day 0), b = bacteria level, antibody signal (used; probably meaningless), y = antibody level, antibody signal smp = whether infection involves big jump small jump t.inf = times infections occurred.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract strata from an object — strata","title":"Extract strata from an object — strata","text":"Generic method extracting strata objects. See strata.seroincidence.()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract strata from an object — strata","text":"","code":"strata(x)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract strata from an object — strata","text":"x object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract strata from an object — strata","text":"strata x","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the Strata attribute from an object, if present — strata.seroincidence.by","title":"Extract the Strata attribute from an object, if present — strata.seroincidence.by","text":"Extract Strata attribute object, present","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the Strata attribute from an object, if present — strata.seroincidence.by","text":"","code":"# S3 method for seroincidence.by strata(x)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the Strata attribute from an object, if present — strata.seroincidence.by","text":"x R object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.seroincidence.by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the Strata attribute from an object, if present — strata.seroincidence.by","text":"tibble::tibble() strata rows, NULL x \"strata\" attribute","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sub-.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or replace parts of a seroincidence.by object — [.seroincidence.by","title":"Extract or replace parts of a seroincidence.by object — [.seroincidence.by","text":"Extract replace parts seroincidence.object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sub-.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or replace parts of a seroincidence.by object — [.seroincidence.by","text":"","code":"# S3 method for seroincidence.by [(x, i, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/sub-.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or replace parts of a seroincidence.by object — [.seroincidence.by","text":"x object subset/replace elements indices subset/replace ... passed [.list","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sub-.seroincidence.by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or replace parts of a seroincidence.by object — [.seroincidence.by","text":"subset specified","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.pop_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize a cross-sectional antibody survey data set — summary.pop_data","title":"Summarize a cross-sectional antibody survey data set — summary.pop_data","text":"function summary() method pop_data objects","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.pop_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize a cross-sectional antibody survey data set — summary.pop_data","text":"","code":"# S3 method for pop_data summary(object, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.pop_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize a cross-sectional antibody survey data set — summary.pop_data","text":"object pop_data object ... unused","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.pop_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize a cross-sectional antibody survey data set — summary.pop_data","text":"list containing two summary tables: one age one value, stratified antigen_iso","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.pop_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize a cross-sectional antibody survey data set — summary.pop_data","text":"","code":"library(dplyr)  xs_data <- \"https://osf.io/download//n6cp3/\" %>% load_pop_data() %>% clean_pop_data()  xs_data %>% summary() #>  #> n = 3336  #>  #> Distribution of age:  #>  #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>    0.60    5.00   10.00   10.53   15.00   25.00  #>  #> Distributions of antigen-isotype measurements: #>  #>   antigen_iso Min   1st Qu.   Median  3rd Qu.      Max # NAs #> 1    HlyE_IgA   0 0.8513305 1.736334 3.656655 133.3666     0 #> 2    HlyE_IgG   0 1.1485953 2.695958 6.737705 218.6229     0"},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Method for ","title":"Summary Method for ","text":"Calculate seroincidence output seroincidence calculator est.incidence.().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Method for ","text":"","code":"# S3 method for seroincidence.by summary(   object,   confidence_level = 0.95,   showDeviance = TRUE,   showConvergence = TRUE,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Method for ","text":"object dataframe containing output function est.incidence.(). confidence_level desired confidence interval coverage probability showDeviance Logical flag (FALSE/TRUE) reporting deviance (-2*log(likelihood) estimated seroincidence. Default = TRUE. showConvergence Logical flag (FALSE/TRUE) reporting convergence (see help optim() details). Default = FALSE. ... Additional arguments affecting summary produced.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Method for ","text":"summary.seroincidence.object, tibble::tibble, following columns: incidence.rate maximum likelihood estimate lambda (seroincidence) CI.lwr lower confidence bound lambda CI.upr upper confidence bound lambda Deviance (included showDeviance = TRUE) Negative log likelihood (NLL) estimated (maximum likelihood) lambda) nlm.convergence.code (included showConvergence = TRUE) Convergence information returned stats::nlm() object also following metadata (accessible base::attr()): antigen_isos Character vector names input antigen isotypes used est.incidence.() Strata Character names strata used est.incidence.()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Method for ","text":"","code":"library(dplyr)  xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\")%>%   clean_pop_data() curve = load_curve_params(\"https://osf.io/download/rtw5k/\" ) noise = load_noise_params(\"https://osf.io/download//hqy4v/\")  est2 = est.incidence.by( strata = c(\"catchment\"), pop_data = xs_data %>% filter(Country == \"Pakistan\"), curve_params = curve, noise_params = noise %>% filter(Country == \"Pakistan\"), antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"), #num_cores = 8 #Allow for parallel processing to decrease run time ) #> data format is as expected. #> Warning: curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning: noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> data format is as expected. #> data format is as expected.  summary(est2) #> Seroincidence estimated given the following setup: #> a) Antigen isotypes   : HlyE_IgG, HlyE_IgA  #> b) Strata       : catchment  #>  #>  Seroincidence estimates: #> # A tibble: 2 × 13 #>   Stratum   catchment     n est.start incidence.rate      SE CI.lwr CI.upr #>   <chr>     <chr>     <int>     <dbl>          <dbl>   <dbl>  <dbl>  <dbl> #> 1 Stratum 1 aku         294       0.1          0.106 0.00767 0.0916  0.122 #> 2 Stratum 2 kgh         200       0.1          0.167 0.0133  0.143   0.196 #> # ℹ 5 more variables: coverage <dbl>, log.lik <dbl>, iterations <int>, #> #   antigen.isos <chr>, nlm.convergence.code <ord>  if (FALSE) { # estimate seroincidence seroincidence <- est.incidence.by(...)  # calculate summary statistics for the seroincidence object seroincidenceSummary <- summary(seroincidence) }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarizing fitted seroincidence models — summary.seroincidence","title":"Summarizing fitted seroincidence models — summary.seroincidence","text":"function summary() method seroincidence objects.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarizing fitted seroincidence models — summary.seroincidence","text":"","code":"# S3 method for seroincidence summary(object, coverage = 0.95, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarizing fitted seroincidence models — summary.seroincidence","text":"object list(), outputted stats::nlm() est.incidence() coverage desired confidence interval coverage probability ... unused","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarizing fitted seroincidence models — summary.seroincidence","text":"tibble::tibble() containing following: est.start: starting guess incidence rate ageCat: age category analyzing incidence.rate: estimated incidence rate, per person year CI.lwr: lower limit confidence interval incidence rate CI.upr: upper limit confidence interval incidence rate coverage: coverage probability log.lik: log-likelihood data used call est.incidence(), evaluated maximum-likelihood estimate lambda (.e., incidence.rate) iterations: number iterations used antigen_isos: list antigen isotypes used analysis nlm.convergence.code: information convergence likelihood maximization procedure performed nlm() (see \"Value\" section stats::nlm(), component code); codes 3-5 indicate issues: 1: relative gradient close zero, current iterate probably solution. 2: successive iterates within tolerance, current iterate probably solution. 3: Last global step failed locate point lower x. Either x approximate local minimum function, function non-linear algorithm, stepmin est.incidence() (.k.., steptol stats::nlm()) large. 4: iteration limit exceeded; increase iterlim. 5: maximum step size stepmax exceeded five consecutive times. Either function unbounded , becomes asymptotic finite value direction stepmax small.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarizing fitted seroincidence models — summary.seroincidence","text":"","code":"library(dplyr)  xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") %>%   clean_pop_data() curve = load_curve_params(\"https://osf.io/download/rtw5k/\") noise = load_noise_params(\"https://osf.io/download//hqy4v/\")  est1 = est.incidence( pop_data = xs_data %>% filter(Country == \"Pakistan\"), curve_param = curve, noise_param = noise %>% filter(Country == \"Pakistan\"), antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\") ) #> data format is as expected.  summary(est1) #> # A tibble: 1 × 10 #>   est.start incidence.rate      SE CI.lwr CI.upr coverage log.lik iterations #>       <dbl>          <dbl>   <dbl>  <dbl>  <dbl>    <dbl>   <dbl>      <int> #> 1       0.1          0.128 0.00682  0.115  0.142     0.95  -2376.          4 #> # ℹ 2 more variables: antigen.isos <chr>, nlm.convergence.code <ord>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/warn.missing.strata.html","id":null,"dir":"Reference","previous_headings":"","what":"Warn about missing stratifying variables in a dataset — warn.missing.strata","title":"Warn about missing stratifying variables in a dataset — warn.missing.strata","text":"Warn missing stratifying variables dataset","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/warn.missing.strata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Warn about missing stratifying variables in a dataset — warn.missing.strata","text":"","code":"warn.missing.strata(data, strata, dataname)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/warn.missing.strata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Warn about missing stratifying variables in a dataset — warn.missing.strata","text":"data dataset contain strata strata data.frame() showing strata levels expected dataset dataname name dataset, use warning messages strata missing.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/warn.missing.strata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Warn about missing stratifying variables in a dataset — warn.missing.strata","text":"character() vector subset stratifying variables present pop_data","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/warn.missing.strata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Warn about missing stratifying variables in a dataset — warn.missing.strata","text":"","code":"if (FALSE) { expected_strata = data.frame(Species = \"banana\", type = \"orchid\") warn.missing.strata(iris, expected_strata, dataname = \"iris\") }"},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-050","dir":"Changelog","previous_headings":"","what":"serocalculator 0.5.0","title":"serocalculator 0.5.0","text":"Spell-checking function documentation tutorial articles. Added functions methods: load_pop_data() check_pop_data() clean_pop_data() summary.pop_data() autoplot.pop_data() load_curve_params() Renamed graph.decay.curves.() autoplot.curve_params()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-040","dir":"Changelog","previous_headings":"","what":"serocalculator 0.4.0","title":"serocalculator 0.4.0","text":"plot() methods renamed autoplot(), matching general convention ggplot2-based graphics. added visualization curve parameters sim.cs() now format argument specify long wide format output.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-0-4-0","dir":"Changelog","previous_headings":"","what":"serocalculator 0.3.2","title":"serocalculator 0.4.0","text":"Fixed bug passing antigen_isos est.incidence.() est.incidence().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-0-4-0-1","dir":"Changelog","previous_headings":"","what":"serocalculator 0.3.1","title":"serocalculator 0.4.0","text":"Rolled back required R version 4.2 4.1","code":""},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-030","dir":"Changelog","previous_headings":"","what":"serocalculator 0.3.0","title":"serocalculator 0.3.0","text":"Fixed stability documentation-clarity issues initial tester feedback.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-020-never-formally-incremented-in-description","dir":"Changelog","previous_headings":"","what":"serocalculator 0.2.0 (never formally incremented in DESCRIPTION)","title":"serocalculator 0.2.0 (never formally incremented in DESCRIPTION)","text":"Added new vignettes.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-010","dir":"Changelog","previous_headings":"","what":"serocalculator 0.1.0","title":"serocalculator 0.1.0","text":"Forking seroincidence package adding Teunis et al 2020 approach.","code":""}]
+[{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement . complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to serocalculator","title":"Contributing to serocalculator","text":"outlines propose change serocalculator. detailed discussion contributing tidyverse packages, please see development contributing guide code review principles.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to serocalculator","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to serocalculator","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed). See guide create great issue advice.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to serocalculator","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"UCD-SERG/serocalculator\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to serocalculator","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to serocalculator","text":"Please note serocalculator project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Enteric Fever Seroincidence Vignette","text":"vignette provides users example analysis using serocalculator package reproducing analysis : Estimating typhoid incidence community-based serosurveys: multicohort study (Aiemjoy et al. (2022)). review methods underlying analysis walk example enteric fever incidence Pakistan.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"example-enteric-fever","dir":"Articles","previous_headings":"","what":"Example: Enteric Fever","title":"Enteric Fever Seroincidence Vignette","text":"example, users determine seroincidence enteric fever cross-sectional serosurveys conducted part SErologic Environmental Surveillance (SEES) enteric fever study Bangladesh, Nepal, Pakistan. Longitudinal antibody responses modeled 1420 blood culture-confirmed enteric fever cases enrolled countries. details published study can found : https://doi.org/10.1016/S2666-5247(22)00114-8.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"load-packages","dir":"Articles","previous_headings":"Example: Enteric Fever","what":"Load packages","title":"Enteric Fever Seroincidence Vignette","text":"first step conducting analysis load necessary packages. haven’t installed already, need loading.","code":"#devtools::install_github(\"ucd-serg/serocalculator\") library(serocalculator) #install.packages(\"tidyverse) library(tidyverse)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"load-data","dir":"Articles","previous_headings":"Example: Enteric Fever","what":"Load data","title":"Enteric Fever Seroincidence Vignette","text":"Pathogen-specific sample datasets, noise parameters, longitudinal antibody dynamics serocalculator available Serocalculator Repository Open Science Framework (OSF). pull data directly R environment. Note dataset specific formatting variable name requirements.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"load-and-prepare-longitudinal-parameter-data","dir":"Articles","previous_headings":"Example: Enteric Fever > Load data","what":"Load and prepare longitudinal parameter data","title":"Enteric Fever Seroincidence Vignette","text":"first load longitudinal curve parameters set antibody decay parameters. example, parameters modeled Bayesian hierarchical models fit two-phase power-function decay models longitudinal antibody responses among confirmed enteric fever cases. Formatting Specifications: Data imported “wide” dataframe one column parameter one row iteration posterior distribution antigen isotype. Column names must exactly match follow naming conventions: Note variable names case-sensitive","code":"#Import longitudinal antibody parameters from OSF  curve =    \"https://osf.io/download/rtw5k/\" %>%    load_curve_params()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"visualize-curve-parameters","dir":"Articles","previous_headings":"Example: Enteric Fever > Load data","what":"Visualize curve parameters","title":"Enteric Fever Seroincidence Vignette","text":"can graph decay curves autoplot() method:","code":"curve %>% autoplot()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"load-and-prepare-cross-sectional-data","dir":"Articles","previous_headings":"Example: Enteric Fever > Load data","what":"Load and prepare cross-sectional data","title":"Enteric Fever Seroincidence Vignette","text":"Next, load sample cross-sectional data. use subset results SEES dataset. Ideally, representative sample general population without regard disease status. Later, limit analysis cross-sectional data Pakistan. selected hemolysin E (HlyE) target antigen IgG IgA target immunoglobulin isotypes. Users may select different serologic markers depending available data. original dataset, rename result age variables names required serocalculator. Formatting Specifications: Cross-sectional population data “long” dataframe one column variable one row antigen isotype resulted individual. individual one row results one antigen isotype. dataframe can additional variables, two required: Note variable names case sensitive","code":"#Import cross-sectional data from OSF and rename required variables xs_data <-    \"https://osf.io/download//n6cp3/\" %>%    load_pop_data() %>%    clean_pop_data()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"check-formatting","dir":"Articles","previous_headings":"Example: Enteric Fever > Load data","what":"Check formatting","title":"Enteric Fever Seroincidence Vignette","text":"can check xs_data correct formatting using check_pop_data() function:","code":"xs_data %>% check_pop_data() #> data format is as expected."},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"summarize-antibody-data","dir":"Articles","previous_headings":"Example: Enteric Fever > Load data","what":"Summarize antibody data","title":"Enteric Fever Seroincidence Vignette","text":"can compute numerical summaries cross-sectional antibody data summary() method pop_data objects:","code":"xs_data %>% summary() #>  #> n = 3336  #>  #> Distribution of age:  #>  #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>    0.60    5.00   10.00   10.53   15.00   25.00  #>  #> Distributions of antigen-isotype measurements: #>  #>   antigen_iso Min   1st Qu.   Median  3rd Qu.      Max # NAs #> 1    HlyE_IgA   0 0.8513305 1.736334 3.656655 133.3666     0 #> 2    HlyE_IgG   0 1.1485953 2.695958 6.737705 218.6229     0"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"visualize-antibody-data","dir":"Articles","previous_headings":"Example: Enteric Fever > Load data","what":"Visualize antibody data","title":"Enteric Fever Seroincidence Vignette","text":"examine cross-sectional antibody data visualizing distribution quantitative antibody responses. , look distribution selected antigen isotype pairs, HlyE IgA HlyE IgG, across participating countries.  see across countries, data highly skewed majority responses lower end data long tails. Let’s get better look distribution log transforming antibody response value.  log transformed, data looks much normally distributed. cases, log transformation best way visualize serologic data. Let’s also take look antibody responses change age.  plot, steeper slope indicates higher incidence. can see highest burden Bangladesh. Nepal slightly higher incidence older group (higher slope).","code":"#Create plots  xs_data %>% autoplot(strata = \"Country\") #Create log transformed plots  options(scipen = 999) xs_data %>%    autoplot(strata = \"Country\", log = TRUE) #> Warning in ggplot2::scale_x_log10(): log-10 transformation #> introduced infinite values. #> Warning: Removed 18 rows containing non-finite outside the scale range #> (`stat_density()`). #Plot antibody responses by age  ggplot(data=xs_data, aes(x=age, y=value, color=Country)) +           geom_point(size=.6, alpha=.7)+          geom_smooth(method=lm, se=FALSE)+          scale_y_log10() +          theme_bw()+           labs(             title = \"Quantitative Antibody Responses by Age\",             x = \"Age\",             y = \"Value\"           ) #> Warning in scale_y_log10(): log-10 transformation introduced infinite values. #> log-10 transformation introduced infinite values. #> Warning: Removed 18 rows containing non-finite outside the scale range #> (`stat_smooth()`)."},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"load-noise-parameters","dir":"Articles","previous_headings":"Example: Enteric Fever","what":"Load noise parameters","title":"Enteric Fever Seroincidence Vignette","text":"Next, must set conditions based assumptions data errors may need accounted . differ based background knowledge data. biological noise, \\(\\nu\\) (“nu”), represents error cross-reactivity antibodies. Measurement noise, \\(\\varepsilon\\) (“epsilon”), represents error laboratory testing process. Formatting Specifications: Noise parameter data dataframe one row antigen isotype columns noise parameter . Note variable names case-sensitive.","code":"#Import noise parameters from OSF  noise <- url(\"https://osf.io/download//hqy4v/\") %>% readRDS()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"estimate-seroincidence","dir":"Articles","previous_headings":"","what":"Estimate Seroincidence","title":"Enteric Fever Seroincidence Vignette","text":"Now ready begin estimating seroincidence. conduct two separate analyses using two distinct functions, est.incidence est.incidence., calculate overall seroincidence stratified seroincidence, respectively.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"overall-seroincidence","dir":"Articles","previous_headings":"Estimate Seroincidence","what":"Overall Seroincidence","title":"Enteric Fever Seroincidence Vignette","text":"Using function est.incidence, filter sites Pakistan define datasets cross-sectional data (pop_data), longitudinal parameters (curve_param), noise parameters (noise_param). also define antigen-isotype pairs included estimate (antigen_isos). , chosen use two antigen isotypes, users can add additional pairs available.","code":"#Using est.incidence (no strata)  est1 = est.incidence(   pop_data = xs_data %>% filter(Country == \"Pakistan\"),   curve_param = curve,    noise_param = noise %>% filter(Country == \"Pakistan\"),   antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\") )  summary(est1) #> # A tibble: 1 × 10 #>   est.start incidence.rate      SE CI.lwr CI.upr coverage log.lik iterations #>       <dbl>          <dbl>   <dbl>  <dbl>  <dbl>    <dbl>   <dbl>      <int> #> 1       0.1          0.128 0.00682  0.115  0.142     0.95  -2376.          4 #> # ℹ 2 more variables: antigen.isos <chr>, nlm.convergence.code <ord>"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"stratified-seroincidence","dir":"Articles","previous_headings":"Estimate Seroincidence","what":"Stratified Seroincidence","title":"Enteric Fever Seroincidence Vignette","text":"can also produce stratified seroincidence estimates. stratify catchment area within Pakistan, users can select stratification variable cross-sectional population dataset. warned “curve_params missing strata variables, used unstratified.” can ignored, unstratified parameters simply give us overall seroincidence estimate. Let’s visualize seroincidence estimates strata.","code":"#Using est.incidence.by (strata)  est2 = est.incidence.by(   strata = c(\"catchment\"),   pop_data = xs_data %>% filter(Country == \"Pakistan\"),   curve_params = curve,    noise_params = noise %>% filter(Country == \"Pakistan\"),   antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"),   num_cores = 8 #Allow for parallel processing to decrease run time    ) #> Warning in warn.missing.strata(data = curve_params, strata = strata %>% : curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning in warn.missing.strata(data = noise_params, strata = strata %>% : noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning in check_parallel_cores(.): This computer appears to have 4 cores #> available. `est.incidence.by()` has reduced its `num_cores` argument to 3 to #> avoid destabilizing the computer.  summary(est2) #> Seroincidence estimated given the following setup: #> a) Antigen isotypes   : HlyE_IgG, HlyE_IgA  #> b) Strata       : catchment  #>  #>  Seroincidence estimates: #> # A tibble: 2 × 13 #>   Stratum   catchment     n est.start incidence.rate      SE CI.lwr CI.upr #>   <chr>     <chr>     <int>     <dbl>          <dbl>   <dbl>  <dbl>  <dbl> #> 1 Stratum 1 aku         294       0.1          0.106 0.00767 0.0916  0.122 #> 2 Stratum 2 kgh         200       0.1          0.167 0.0133  0.143   0.196 #> # ℹ 5 more variables: coverage <dbl>, log.lik <dbl>, iterations <int>, #> #   antigen.isos <chr>, nlm.convergence.code <ord> #Plot seroincidence estimates  #Save summary(est2) as a dataframe est2df<- summary(est2)  #Create barplot (rescale incidence rate and CIs) ggplot(est2df, aes(y=catchment, x=incidence.rate*1000, fill=catchment)) +   geom_bar(stat = \"identity\", show.legend = FALSE) +   geom_errorbar(aes(xmin =CI.lwr*1000, xmax=CI.upr*1000, width=.05))+   labs(       title= \"Enteric Fever Seroincidence by Catchment Area\",         x=\"Seroincience rate per 1000 person-years\",         y=\"Catchment\"       ) +   theme_bw() +   theme(axis.text.y = element_text(size=11),         axis.text.x = element_text(size=11))"},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"conclusions","dir":"Articles","previous_headings":"","what":"Conclusions","title":"Enteric Fever Seroincidence Vignette","text":"data, find overall estimated seroincidence enteric fever Pakistan 153 per 1000 person-years (95% CI: 139, 169). stratified catchment area, find area KGH higher incidence rate area AKU [204 per 1000 person-years (95% CI: 176, 237) vs. 125 per 1000 person-years (95% CI: 109, 143)].","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/enteric_fever_example.html","id":"acknowledgments","dir":"Articles","previous_headings":"","what":"Acknowledgments","title":"Enteric Fever Seroincidence Vignette","text":"Special thanks collaborators Aga Khan University (Karachi, Pakistan), Child Health Research Foundation (Dhaka, Bangladesh), Dhulikhel Hospital, Kathmandu University Hospital (Dhulikhel, Nepal).","code":""},{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Scrub Typhus Seroincidence Vignette","text":"vignette reproduces analysis : Estimating seroincidence scrub typhus using antibody dynamics following infection (Aiemjoy et al. (Accepted Feb 2024)).","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"methods","dir":"Articles","previous_headings":"","what":"Methods","title":"Scrub Typhus Seroincidence Vignette","text":"serocalculator R package provides rapid computationally simple method calculating seroconversion rates, originally published Simonsen et al. (2009) Teunis et al. (2012), developed subsequent publications deGraaf et al. (2014), Teunis et al. (2016), Teunis Eijkeren (2020). short, longitudinal seroresponses confirmed cases known symptom onset date assumed represent time course human serum antibodies specific pathogen. Therefore, using longitudinal antibody dynamics cross–sectional sample antibodies human population, incidence estimate can calculated.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"the-seroincidence-estimator","dir":"Articles","previous_headings":"Methods","what":"The Seroincidence Estimator","title":"Scrub Typhus Seroincidence Vignette","text":"serocalculator package designed calculate incidence seroconversion using longitudinal seroresponse characteristics. distribution serum antibody concentrations cross–sectional population sample calculated function longitudinal seroresponse frequency seroconversion (seroincidence). Given seroresponse, marginal distribution antibody concentrations can fitted cross-sectional data thereby providing means estimate seroincidence.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"scrub-typhus-seroincidence","dir":"Articles","previous_headings":"","what":"Scrub Typhus Seroincidence","title":"Scrub Typhus Seroincidence Vignette","text":"Scrub typhus, vector-borne bacterial infection, important neglected disease globally. Accurately characterizing burden challenging due non-specific symptoms limited diagnostics. Prior seroepidemiology studies struggled find consensus cutoffs permit comparing estimates across contexts time. study, present novel approach require cutoff instead uses information antibody kinetics infection estimate seroincidence. use data three cohorts scrub typhus patients Chiang Rai, Thailand, Vellore, India characterize antibody kinetics infection two population serosurveys Kathmandu valley, Nepal, Tamil Nadu, India estimate seroincidence. samples tested IgM IgG responses Orientia tsutsugamushi-derived recombinant 56-kDa antigen using commercial ELISA kits. used Bayesian hierarchical models characterize antibody responses scrub typhus infection used joint distributions peak antibody titers decay rates estimate population-level incidence rates cross-sectional serosurveys.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"load-packages","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence","what":"Load packages","title":"Scrub Typhus Seroincidence Vignette","text":"first step conducting analysis load necessary packages. haven’t installed already, need loading.","code":"#devtools::install_github(\"ucd-serg/serocalculator\") library(serocalculator) #install.packages(\"tidyverse\") library(tidyverse) #install.packages(\"mixtools\") library(mixtools)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"load-data","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence","what":"Load data","title":"Scrub Typhus Seroincidence Vignette","text":"Pathogen-specific sample datasets, noise parameters, longitudinal antibody dynamics serocalculator available Serocalculator Repository Open Science Framework (OSF). pull data directly R environment.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"load-and-prepare-longitudinal-parameter-data","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence > Load data","what":"Load and prepare longitudinal parameter data","title":"Scrub Typhus Seroincidence Vignette","text":"first load longitudinal curve parameters set antibody decay parameters. parameters modeled Bayesian hierarchical models fit two-phase power-function decay models longitudinal antibody responses among confirmed enteric fever cases.","code":"#Import longitudinal antibody parameters from OSF  curve <-     \"https://osf.io/download/u5gxh/\" %>%    load_curve_params()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"visualize-curve-parameters","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence > Load data","what":"Visualize curve parameters","title":"Scrub Typhus Seroincidence Vignette","text":"can graph decay curves autoplot() method:","code":"curve %>% autoplot()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"load-and-prepare-cross-sectional-data","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence > Load data","what":"Load and prepare cross-sectional data","title":"Scrub Typhus Seroincidence Vignette","text":"Next, load sample cross-sectional data.","code":"#Import cross-sectional data from OSF and rename required variables xs_data <-    \"https://osf.io/download/h5js4/\" %>%    load_pop_data() %>%    clean_pop_data()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"check-formatting","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence > Load data","what":"Check formatting","title":"Scrub Typhus Seroincidence Vignette","text":"can check xs_data correct formatting using check_pop_data() function:","code":"xs_data %>% check_pop_data() #> data format is as expected."},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"summarize-antibody-data","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence > Load data","what":"Summarize antibody data","title":"Scrub Typhus Seroincidence Vignette","text":"can compute numerical summaries cross-sectional antibody data summary() method pop_data objects:","code":"xs_data %>% summary() #>  #> n = 1608  #>  #> Distribution of age:  #>  #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>    0.90   10.18   21.00   29.02   46.25   87.00  #>  #> Distributions of antigen-isotype measurements: #>  #>   antigen_iso   Min 1st Qu. Median 3rd Qu.   Max # NAs #> 1 OT56kda_IgG 0.050 0.25025 0.4345 0.67875 3.814     0 #> 2 OT56kda_IgM 0.051 0.10000     NA 0.22900 3.599  1105"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"visualize-antibody-data","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence > Load data","what":"Visualize antibody data","title":"Scrub Typhus Seroincidence Vignette","text":"Let’s also take look antibody responses change age.","code":"#Plot antibody responses by age  ggplot(data=xs_data, aes(x=age, y=value, color=country)) +           geom_point(size=.6, alpha=.7)+          geom_smooth(method=lm, se=FALSE)+          scale_y_log10() +          theme_bw()+           labs(             title = \"Quantitative Antibody Responses by Age\",             x = \"Age\",             y = \"Value\"           ) +    facet_wrap(~antigen_iso) +   scale_color_manual(values = c(\"orange2\",'#39558CFF')) #> Warning: Removed 1105 rows containing non-finite outside the scale range #> (`stat_smooth()`). #> Warning: Removed 1105 rows containing missing values or values outside the scale range #> (`geom_point()`)."},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"compile-noise-parameters","dir":"Articles","previous_headings":"Scrub Typhus Seroincidence","what":"Compile noise parameters","title":"Scrub Typhus Seroincidence Vignette","text":"Next, must set conditions based assumptions data errors may need accounted . differ based background knowledge data. biological noise, \\(\\nu\\) (“nu”), represents error cross-reactivity antibodies. Measurement noise, \\(\\varepsilon\\) (“epsilon”), represents error laboratory testing process. Formatting Specifications: Noise parameter data dataframe one row antigen isotype columns noise parameter . Note variable names case-sensitive.","code":"#biologic noise b.noise <- xs_data %>%   group_by(antigen_iso) %>%   filter(!is.na(value)) %>%   filter(age<40) %>% # restrict to young ages to capture recent exposures    do({     set.seed(54321)     # Fit the mixture model     mixmod <- normalmixEM(.$value, k = 2, maxit = 1000) # k is the number of components, adjust as necessary     # Assuming the first component is the lower distribution     lower_mu <- mixmod$mu[1]     lower_sigma <- sqrt(mixmod$sigma[1])     # Calculate the 90th percentile of the lower distribution     percentile75 <- qnorm(0.75, lower_mu, lower_sigma)     # Return the results     data.frame(antigen_iso = .$antigen_iso[1], percentile75 = percentile75)   }) #> number of iterations= 35  #> number of iterations= 24    # define conditional parameters noise <- data.frame(   antigen_iso = c(\"OT56kda_IgG\", \"OT56kda_IgM\"),   nu = as.numeric(c(b.noise[2,2], b.noise[1,2])),  # Biologic noise (nu)   eps = c(0.2, 0.2),                             # M noise (eps)   y.low = c(0.2, 0.2),                            # low cutoff (llod)   y.high = c(200, 200)) %>%                      # high cutoff (y.high)   mutate(across(where(is.numeric), round, digits = 2))"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"estimate-seroincidence-by-study-site","dir":"Articles","previous_headings":"","what":"Estimate Seroincidence by study site","title":"Scrub Typhus Seroincidence Vignette","text":"Now ready begin estimating seroincidence. use est.incidence.calculate stratified seroincidence rates.","code":"#Using est.incidence.by (strata)  est = est.incidence.by(   strata = c(\"country\"),   pop_data = xs_data,   curve_params = curve,    noise_params = noise,   antigen_isos = c(\"OT56kda_IgG\"),   num_cores = 8 #Allow for parallel processing to decrease run time    ) #> Warning in warn.missing.strata(data = curve_params, strata = strata %>% : curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning in warn.missing.strata(data = noise_params, strata = strata %>% : noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning in check_parallel_cores(.): This computer appears to have 4 cores #> available. `est.incidence.by()` has reduced its `num_cores` argument to 3 to #> avoid destabilizing the computer.  summary(est) #> Seroincidence estimated given the following setup: #> a) Antigen isotypes   : OT56kda_IgG  #> b) Strata       : country  #>  #>  Seroincidence estimates: #> # A tibble: 2 × 13 #>   Stratum country     n est.start incidence.rate      SE  CI.lwr CI.upr coverage #>   <chr>   <fct>   <int>     <dbl>          <dbl>   <dbl>   <dbl>  <dbl>    <dbl> #> 1 Stratu… India     721       0.1        0.0202  0.00148 0.0175  0.0233     0.95 #> 2 Stratu… Nepal    1105       0.1        0.00854 0.00107 0.00668 0.0109     0.95 #> # ℹ 4 more variables: log.lik <dbl>, iterations <int>, antigen.isos <chr>, #> #   nlm.convergence.code <ord>"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"estimate-seroincidence-by-study-site-and-age-strata","dir":"Articles","previous_headings":"","what":"Estimate Seroincidence by study site and age strata","title":"Scrub Typhus Seroincidence Vignette","text":"Now ready begin estimating seroincidence. use est.incidence.calculate stratified seroincidence rates. Let’s visualize seroincidence estimates strata.","code":"#Using est.incidence.by (strata)  est2 = est.incidence.by(   strata = c(\"country\", \"ageQ\"),   pop_data = xs_data,   curve_params = curve,    noise_params = noise,   antigen_isos = c(\"OT56kda_IgG\"),   num_cores = 8 #Allow for parallel processing to decrease run time    ) #> Warning in warn.missing.strata(data = curve_params, strata = strata %>% : curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning in warn.missing.strata(data = noise_params, strata = strata %>% : noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning in check_parallel_cores(.): This computer appears to have 4 cores #> available. `est.incidence.by()` has reduced its `num_cores` argument to 3 to #> avoid destabilizing the computer.  summary(est2) #> Seroincidence estimated given the following setup: #> a) Antigen isotypes   : OT56kda_IgG  #> b) Strata       : country, ageQ  #>  #>  Seroincidence estimates: #> # A tibble: 5 × 14 #>   Stratum   country ageQ      n est.start incidence.rate      SE  CI.lwr  CI.upr #>   <chr>     <fct>   <fct> <int>     <dbl>          <dbl>   <dbl>   <dbl>   <dbl> #> 1 Stratum 1 India   18-29    59       0.1        0.00985 0.00330 0.00511 0.0190  #> 2 Stratum 2 India   30-49   302       0.1        0.0151  0.00178 0.0120  0.0190  #> 3 Stratum 3 India   50-89   360       0.1        0.0288  0.00290 0.0236  0.0350  #> 4 Stratum 4 Nepal   0-17    876       0.1        0.00606 0.00108 0.00428 0.00860 #> 5 Stratum 5 Nepal   18-29   229       0.1        0.0144  0.00259 0.0101  0.0205  #> # ℹ 5 more variables: coverage <dbl>, log.lik <dbl>, iterations <int>, #> #   antigen.isos <chr>, nlm.convergence.code <ord> #Plot seroincidence estimates  #Save summary(est) as a dataframe estdf<- summary(est) %>%   mutate(ageQ = \"Overall\")  #Save summary(est2) as a dataframe est2df<- summary(est2)    est.comb <- rbind(estdf, est2df)  #Create barplot (rescale incidence rate and CIs) ggplot(est.comb, aes(y=ageQ, x=incidence.rate*1000, fill=country)) +   geom_bar(stat = \"identity\", position = position_dodge2(width = 0.8, preserve = \"single\")) +   geom_linerange(aes(xmin =CI.lwr*1000, xmax=CI.upr*1000),                  position = position_dodge2(width = 0.8, preserve = \"single\")) +   labs(       title= \"Enteric Fever Seroincidence by Catchment Area\",         x=\"Seroincience rate per 1000 person-years\",         y=\"Catchment\"       ) +   theme_bw() +   #facet_wrap(~country) +    theme(axis.text.y = element_text(size=11),         axis.text.x = element_text(size=11)) +    scale_fill_manual(values = c(\"orange2\",'#39558CFF'))"},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"acknowledgments","dir":"Articles","previous_headings":"","what":"Acknowledgments","title":"Scrub Typhus Seroincidence Vignette","text":"gratefully acknowledge study participants valuable time interest participating studies","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/scrubTyphus_example.html","id":"funding","dir":"Articles","previous_headings":"","what":"Funding","title":"Scrub Typhus Seroincidence Vignette","text":"work supported National Institutes Health Fogarty International Center (FIC) [K01 TW012177] National Institute Allergy Infectious Diseases (NIAID) [R21 1AI176416]","code":""},{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/articles/serocalculator.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Methodology","text":"serocalculator R package provides rapid computationally simple method calculating seroconversion rates, originally published Simonsen et al. (2009) Teunis et al. (2012), developed subsequent publications deGraaf et al. (2014), Teunis et al. (2016), Teunis Eijkeren (2020). short, longitudinal seroresponses confirmed cases known symptom onset date assumed represent time course human serum antibodies specific pathogen. Therefore, using longitudinal antibody dynamics cross–sectional sample antibodies human population, incidence estimate can calculated. details .","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/serocalculator.html","id":"a-proxy-for-infection","dir":"Articles","previous_headings":"Overview","what":"A Proxy for Infection","title":"Methodology","text":"exact time infection impossible measure individual, antibody levels measured cross–sectional population sample can translated estimate frequency seroconversions (infections) occur sampled population. presence many high antibody concentrations indicates many people population likely experienced infection recently, mostly low concentrations indicate low frequency infections sampled population. order interpret measured cross-sectional antibody concentrations terms incidence, must define antibody dynamic time understand generalized antibody response different times since infection. dynamic must quantified time include initial increase serum antibody concentration seroconversion occurs, followed gradual decrease antibodies wane. published studies, information time course serum antibody response obtained longitudinal follow–data cases symptomatic episode following infection. case, onset symptoms provides proxy time infection occurred.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/articles/serocalculator.html","id":"the-seroincidence-estimator","dir":"Articles","previous_headings":"Overview","what":"The Seroincidence Estimator","title":"Methodology","text":"serocalculator package designed calculate incidence seroconversion using longitudinal seroresponse characteristics. distribution serum antibody concentrations cross–sectional population sample calculated function longitudinal seroresponse frequency seroconversion (seroincidence). Given seroresponse, marginal distribution antibody concentrations can fitted cross-sectional data thereby providing means estimate seroincidence.","code":""},{"path":[]},{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"load-model-parameters","dir":"Articles","previous_headings":"Simulate a single dataset","what":"load model parameters","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"","code":"library(serocalculator) library(tidyverse) #> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── #> ✔ dplyr     1.1.4     ✔ readr     2.1.5 #> ✔ forcats   1.0.0     ✔ stringr   1.5.1 #> ✔ ggplot2   3.5.0     ✔ tibble    3.2.1 #> ✔ lubridate 1.9.3     ✔ tidyr     1.3.1 #> ✔ purrr     1.0.2      #> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── #> ✖ dplyr::filter() masks stats::filter() #> ✖ dplyr::lag()    masks stats::lag() #> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors library(ggbeeswarm) # for plotting  #load in longitudinal parameters, these are modeled from all SEES cases across all ages and countries  dmcmc =   \"https://osf.io/download/rtw5k\" %>%    load_curve_params()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"visualize-antibody-decay-model","dir":"Articles","previous_headings":"Simulate a single dataset","what":"visualize antibody decay model","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"can graph individual MCMC samples posterior distribution model parameters using autoplot.curve_params() method autoplot() function:  can use logarithmic scale x-axis desired:  can graph median, 10%, 90% quantiles model using graph.curve.params() function:","code":"dmcmc %>% autoplot() dmcmc %>% autoplot(log_x = TRUE) # Specify the antibody-isotype responses to include in analyses antibodies = c(\"HlyE_IgA\", \"HlyE_IgG\")  dmcmc %>%    graph.curve.params(antigen_isos = antibodies) %>%    print() #> Warning: Removed 57 rows containing missing values or values outside the scale range #> (`geom_line()`)."},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"simulate-cross-sectional-data","dir":"Articles","previous_headings":"Simulate a single dataset","what":"Simulate cross-sectional data","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"","code":"#set seed to reproduce results set.seed(54321)  # simulated incidence rate per person-year lambda <- 0.2;  # range covered in simulations lifespan <- c(0, 10);   # cross-sectional sample size nrep <- 100  # biologic noise distribution dlims <- rbind(   \"HlyE_IgA\" = c(min = 0, max = 0.5),   \"HlyE_IgG\" = c(min = 0, max = 0.5)) #generate cross-sectional data csdata <- sim.cs(   curve_params = dmcmc,   lambda = lambda,   n.smpl = nrep,   age.rng = lifespan,   antigen_isos = antibodies,   n.mc = 0,   renew.params = TRUE,   add.noise = TRUE,   noise_limits = dlims,   format = \"long\" )"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"noise-parameters","dir":"Articles","previous_headings":"Simulate a single dataset","what":"Noise parameters","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"need provide noise parameters analysis; , define directly code:","code":"library(tibble) cond <- tibble(   antigen_iso = c(\"HlyE_IgG\", \"HlyE_IgA\"),   nu = c(0.5, 0.5),                          # Biologic noise (nu)   eps = c(0, 0),                             # M noise (eps)   y.low = c(1, 1),                           # low cutoff (llod)   y.high = c(5e6, 5e6))                      # high cutoff (y.high)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"visualize-data","dir":"Articles","previous_headings":"Simulate a single dataset","what":"Visualize data","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"can plot distribution antibody responses simulated data.","code":"ggplot(csdata, aes(x=as.factor(antigen_iso), y= value)) +   geom_beeswarm(size= .2, alpha = .3, aes(color = antigen_iso), show.legend = F) +    geom_boxplot(outlier.colour = NA, fill = NA) +    scale_y_log10() +    theme_linedraw() +    labs(x=\"antigen - isotype\")"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"calculate-log-likelihood","dir":"Articles","previous_headings":"Simulate a single dataset","what":"calculate log-likelihood","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"can calculate log-likelihood data function incidence rate directly:","code":"ll_A = llik(   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = \"HlyE_IgA\",   lambda = 0.1) %>% print() #> [1] -226.1281  ll_G = llik(   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = \"HlyE_IgG\",   lambda = 0.1) %>% print() #> [1] -327.432  ll_AG = llik(   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = c(\"HlyE_IgG\",\"HlyE_IgA\"),   lambda = 0.1) %>% print() #> [1] -553.5601  print(ll_A + ll_G) #> [1] -553.5601"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"graph-log-likelihood","dir":"Articles","previous_headings":"Simulate a single dataset","what":"graph log-likelihood","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"can also graph log-likelihoods, even without finding MLEs, using graph.loglik():","code":"lik_HlyE_IgA = graph.loglik(   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = \"HlyE_IgA\",   log_x = TRUE )  lik_HlyE_IgG = graph.loglik(   previous_plot = lik_HlyE_IgA,   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = \"HlyE_IgG\",   log_x = TRUE )  lik_both = graph.loglik(   previous_plot = lik_HlyE_IgG,   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"),   log_x = TRUE )  print(lik_both)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"estimate-incidence","dir":"Articles","previous_headings":"Simulate a single dataset","what":"estimate incidence","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"can estimate incidence est.incidence(): can extract summary statistics summary(): can plot log-likelihood curve autoplot():  can set x-axis logarithmic scale:","code":"est1 = est.incidence(   pop_data = csdata,   curve_params = dmcmc,   noise_params = cond,   lambda_start = .1,   build_graph = T,   verbose = T, # print updates as the function runs   print_graph = F, # display the log-likelihood curve while `est.incidence()` is running   antigen_isos = antibodies) #> data format is as expected. #> nrow(curve_params) = 8000 #> Initial negative log-likelihood: 553.560077627804 #> building likelihood graph #> about to call `nlm()` #> iteration = 0 #> Step: #> [1] 0 #> Parameter: #> [1] -2.302585 #> Function Value #> [1] 553.5601 #> Gradient: #> [1] -42.3037 #>  #> iteration = 1 #> Step: #> [1] 0.4769564 #> Parameter: #> [1] -1.825629 #> Function Value #> [1] 539.2395 #> Gradient: #> [1] -15.99511 #>  #> iteration = 2 #> Step: #> [1] 0.2899803 #> Parameter: #> [1] -1.535648 #> Function Value #> [1] 537.6454 #> Gradient: #> [1] 5.827356 #>  #> iteration = 3 #> Step: #> [1] -0.07743479 #> Parameter: #> [1] -1.613083 #> Function Value #> [1] 537.4411 #> Gradient: #> [1] -0.4900419 #>  #> iteration = 4 #> Step: #> [1] 0.006006633 #> Parameter: #> [1] -1.607077 #> Function Value #> [1] 537.4396 #> Gradient: #> [1] -0.01308702 #>  #> iteration = 5 #> Parameter: #> [1] -1.606912 #> Function Value #> [1] 537.4396 #> Gradient: #> [1] 3.275662e-05 #>  #> Relative gradient close to zero. #> Current iterate is probably solution. #>  #> Elapsed time: #>    user  system elapsed  #>   1.554   0.000   1.554 summary(est1) #> # A tibble: 1 × 10 #>   est.start incidence.rate     SE CI.lwr CI.upr coverage log.lik iterations #>       <dbl>          <dbl>  <dbl>  <dbl>  <dbl>    <dbl>   <dbl>      <int> #> 1       0.1          0.201 0.0225  0.161  0.250     0.95   -537.          5 #> # ℹ 2 more variables: antigen.isos <chr>, nlm.convergence.code <ord> autoplot(est1) autoplot(est1, log_x = TRUE)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"simulate-multiple-clusters-with-different-lambdas","dir":"Articles","previous_headings":"","what":"Simulate multiple clusters with different lambdas","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"preceding code chunk, determined can use 3 CPU cores run computations parallel. can plot distributions simulated responses:","code":"library(parallel) n_cores = max(1, parallel::detectCores() - 1) # n_cores = 1 print(n_cores) #> [1] 3 #number of clusters  nclus = 10;  # cross-sectional sample size nrep <- 100  #incidence rate in e lmbdaVec <- c(.05,.1, .15, .2)  sim.df =    sim.cs.multi(     # verbose = TRUE,     n_cores = n_cores,     lambdas = lmbdaVec,     nclus = nclus,     n.smpl = nrep,     age.rng = lifespan,     antigen_isos = antibodies,     renew.params = TRUE,     add.noise = TRUE,     curve_params = dmcmc,     noise_limits = dlims,     format = \"long\"   )  print(sim.df) #> # A tibble: 8,000 × 5 #>      age antigen_iso value lambda.sim cluster #>    <dbl> <chr>       <dbl>      <dbl>   <int> #>  1  3.53 HlyE_IgA    0.538       0.05       1 #>  2  3.53 HlyE_IgG    0.592       0.05       1 #>  3  2.27 HlyE_IgA    0.620       0.05       1 #>  4  2.27 HlyE_IgG    0.309       0.05       1 #>  5  9.05 HlyE_IgA    0.464       0.05       1 #>  6  9.05 HlyE_IgG    0.638       0.05       1 #>  7  5.94 HlyE_IgA    0.417       0.05       1 #>  8  5.94 HlyE_IgG    0.662       0.05       1 #>  9  9.88 HlyE_IgA    0.185       0.05       1 #> 10  9.88 HlyE_IgG    0.278       0.05       1 #> # ℹ 7,990 more rows ggplot(sim.df, aes(x = as.factor(cluster), y= value)) +   geom_beeswarm(size= .2, alpha = .3, aes(color = antigen_iso)) +    geom_boxplot(outlier.colour = NA, fill = NA) +    scale_y_log10() +    facet_wrap(~antigen_iso + lambda.sim, nrow = 2) +    theme_linedraw()"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"estimate-incidence-in-each-cluster","dir":"Articles","previous_headings":"Simulate multiple clusters with different lambdas","what":"Estimate incidence in each cluster","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"summary(ests) produces tibble() extra meta-data: can explore summary table interactively using DT::datatable() can plot likelihood single simulated cluster subsetting simulation ests calling plot():  can also plot log-likelihood curves several clusters (computer might struggle plot many ):  log_x argument also works :","code":"ests =   sim.df %>%    est.incidence.by(     curve_params = dmcmc,     noise_params = cond,     num_cores = n_cores,     strata = c(\"lambda.sim\", \"cluster\"),     curve_strata_varnames = NULL,     noise_strata_varnames = NULL,     verbose = TRUE,     build_graph = TRUE, # slows down the function substantially     antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\")     # antigen_isos = \"HlyE_IgA\"   ) #> data format is as expected. #> Data has been stratified. #> Here are the strata that will be analyzed: #> # A tibble: 40 × 4 #>    Stratum    lambda.sim cluster     n #>    <chr>           <dbl>   <int> <int> #>  1 Stratum 1        0.05       1   100 #>  2 Stratum 2        0.05       2   100 #>  3 Stratum 3        0.05       3   100 #>  4 Stratum 4        0.05       4   100 #>  5 Stratum 5        0.05       5   100 #>  6 Stratum 6        0.05       6   100 #>  7 Stratum 7        0.05       7   100 #>  8 Stratum 8        0.05       8   100 #>  9 Stratum 9        0.05       9   100 #> 10 Stratum 10       0.05      10   100 #> # ℹ 30 more rows #> Setting up parallel processing with `num_cores` = 3. #> Elapsed time for parallelized code: #>    user  system elapsed  #>   0.268   0.048  93.030 summary(ests) #> Warning in FUN(X[[i]], ...): `nlm()` produced a negative hessian; something is wrong with the numerical derivatives. #> The standard error of the incidence rate estimate cannot be calculated. #> Warning in sqrt(var.log.lambda): NaNs produced #> Seroincidence estimated given the following setup: #> a) Antigen isotypes   : HlyE_IgG, HlyE_IgA  #> b) Strata       : lambda.sim, cluster  #>  #>  Seroincidence estimates: #> # A tibble: 40 × 14 #>    Stratum    lambda.sim cluster     n est.start incidence.rate      SE CI.lwr #>    <chr>           <dbl>   <int> <int>     <dbl>          <dbl>   <dbl>  <dbl> #>  1 Stratum 1        0.05       1   100       0.1         0.0367 0.00686 0.0254 #>  2 Stratum 2        0.05       2   100       0.1         0.0423 0.00778 0.0295 #>  3 Stratum 3        0.05       3   100       0.1         0.0648 0.00992 0.0480 #>  4 Stratum 4        0.05       4   100       0.1         0.0665 0.0107  0.0484 #>  5 Stratum 5        0.05       5   100       0.1         0.0390 0.00769 0.0265 #>  6 Stratum 6        0.05       6   100       0.1         0.0474 0.00846 0.0334 #>  7 Stratum 7        0.05       7   100       0.1         0.0684 0.0105  0.0506 #>  8 Stratum 8        0.05       8   100       0.1         0.0309 0.00643 0.0206 #>  9 Stratum 9        0.05       9   100       0.1         0.0466 0.00827 0.0329 #> 10 Stratum 10       0.05      10   100       0.1         0.0461 0.00824 0.0325 #> # ℹ 30 more rows #> # ℹ 6 more variables: CI.upr <dbl>, coverage <dbl>, log.lik <dbl>, #> #   iterations <int>, antigen.isos <chr>, nlm.convergence.code <ord> library(DT) summary(ests) %>%    DT::datatable() %>%   DT::formatRound(     columns = c(       \"incidence.rate\",       \"SE\",       \"CI.lwr\",       \"CI.upr\",       \"log.lik\"     )   ) #> Warning in FUN(X[[i]], ...): `nlm()` produced a negative hessian; something is wrong with the numerical derivatives. #> The standard error of the incidence rate estimate cannot be calculated. #> Warning in sqrt(var.log.lambda): NaNs produced autoplot(ests[1]) autoplot(ests[1:5]) autoplot(ests[1:5], log_x = TRUE)"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"nlm-convergence-codes","dir":"Articles","previous_headings":"Simulate multiple clusters with different lambdas > Estimate incidence in each cluster","what":"nlm() convergence codes","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"Make sure check nlm() exit codes (codes 3-5 indicate possible non-convergence): Solutions nlm() exit codes 3-5: 3: decrease stepmin argument est.incidence()/est.incidence.() 4: increase iterlim argument est.incidence()/est.incidence.() 5: increase stepmax argument est.incidence()/est.incidence.() can extract indices problematic strata, : clusters problems, can take look: fits don’t appear maximum likelihood, re-run clusters, adjusting nlm() settings appropriately, sure.","code":"summary(ests) %>%   as_tibble() %>% # removes extra meta-data   select(Stratum, nlm.convergence.code) %>%    filter(nlm.convergence.code > 2)  #> Warning in FUN(X[[i]], ...): `nlm()` produced a negative hessian; something is wrong with the numerical derivatives. #> The standard error of the incidence rate estimate cannot be calculated. #> Warning in sqrt(var.log.lambda): NaNs produced #> # A tibble: 0 × 2 #> # ℹ 2 variables: Stratum <chr>, nlm.convergence.code <ord> problem_strata =    which(summary(ests)$nlm.convergence.code > 2) %>%    print() #> Warning in FUN(X[[i]], ...): `nlm()` produced a negative hessian; something is wrong with the numerical derivatives. #> The standard error of the incidence rate estimate cannot be calculated. #> Warning in sqrt(var.log.lambda): NaNs produced #> integer(0) if(length(problem_strata) > 0) {   autoplot(ests[problem_strata], log_x = TRUE) }"},{"path":"https://ucd-serg.github.io/serocalculator/articles/simulate_xsectionalData.html","id":"plot-distribution-of-estimates-by-simulated-incidence-rate","dir":"Articles","previous_headings":"Simulate multiple clusters with different lambdas","what":"plot distribution of estimates by simulated incidence rate","title":"Generate a simulated cross-sectional sample and estimate seroincidence","text":"Finally, can look simulation results:","code":"library(ggplot2) summary(ests) %>%    autoplot(xvar = 'lambda.sim') +   ggplot2::geom_abline(     ggplot2::aes(intercept = 0, slope = 1)) #> Warning in FUN(X[[i]], ...): `nlm()` produced a negative hessian; something is wrong with the numerical derivatives. #> The standard error of the incidence rate estimate cannot be calculated. #> Warning in sqrt(var.log.lambda): NaNs produced"},{"path":"https://ucd-serg.github.io/serocalculator/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Peter Teunis. Author, copyright holder.            Author method original code. Kristina Lai. Author, maintainer. Kristen Aiemjoy. Author. Douglas Ezra Morrison. Author.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Teunis P, Lai K, Aiemjoy K, Morrison D (2024). serocalculator: Estimating Infection Rates Serological Data. R package version 1.0.0, https://ucd-serg.github.io/serocalculator/, https://github.com/UCD-SERG/serocalculator.","code":"@Manual{,   title = {serocalculator: Estimating Infection Rates from Serological Data},   author = {Peter Teunis and Kristina Lai and Kristen Aiemjoy and Douglas Ezra Morrison},   year = {2024},   note = {R package version 1.0.0, https://ucd-serg.github.io/serocalculator/},   url = {https://github.com/UCD-SERG/serocalculator}, }"},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"serocalculator","dir":"","previous_headings":"","what":"Estimating Infection Rates from Serological Data","title":"Estimating Infection Rates from Serological Data","text":"Antibody levels measured cross–sectional population sample can translated estimate frequency seroconversions (infections) occur sampled population. words, presence many high antibody titers indicates many individuals likely experienced infection recently burden disease high population, low titers indicate low frequency infections sampled population therefore lower burden disease. serocalculator package designed use longitudinal response characteristics using set modeled parameters characterizing longitudinal response selected serum antibodies. details underlying methods can found Getting Started.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"installing-r","dir":"","previous_headings":"","what":"Installing R","title":"Estimating Infection Rates from Serological Data","text":"serocalculator package written R, free, open-source software program. end user package must access working installation R software. recommend installing base R Graphical User Interfaces (GUI) R RStudio. need download install R /RStudio, recommend following tutorial Hands Programming R Garrett Grolemund: Installing R RStudio: https://rstudio-education.github.io/hopr/starting.html","code":""},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"installing-the-serocalculator-package","dir":"","previous_headings":"","what":"Installing the Serocalculator Package","title":"Estimating Infection Rates from Serological Data","text":"serocalculator package must installed R first use.","code":"# Install package install.packages(\"serocalculator\")"},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"post-installation","dir":"","previous_headings":"Installing the Serocalculator Package","what":"Post-installation","title":"Estimating Infection Rates from Serological Data","text":"Successful installation can confirmed loading package RStudio workspaceand exploring help files manuals distributed package: Additionally, package details can found executing following commands:","code":"# Load package \"seroincidence\". library(serocalculator)  # Show R help for the package. ?serocalculator # Show description. packageDescription(\"serocalculator\")  # Show citation. citation(\"serocalculator\")"},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"development-version","dir":"","previous_headings":"Installing the Serocalculator Package","what":"Development Version","title":"Estimating Infection Rates from Serological Data","text":"install development version, must install devtools R package download serocalculator GitHub. Enter code R console install packages.","code":"# Install the devtools package and the development version of serocalculator install.packages(\"devtools\") devtools::install_github(\"ucd-serg/serocalculator\")"},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"a-note-for-windows-users","dir":"","previous_headings":"Installing the Serocalculator Package > Development Version","what":"A Note for Windows Users","title":"Estimating Infection Rates from Serological Data","text":"launching development version serocalculator, Windows users need install Rtools, contains collection tools building employing R packages still development. can done either devtools package installation, independently devtools already installed.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"during-devtools-installation","dir":"","previous_headings":"Installing the Serocalculator Package > Development Version > A Note for Windows Users","what":"During devtools installation:","title":"Estimating Infection Rates from Serological Data","text":"prompted install additional build tools, select “Yes” Rtools installed. Click Yes install Rtools along devtools package","code":""},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"independently","dir":"","previous_headings":"Installing the Serocalculator Package > Development Version > A Note for Windows Users","what":"Independently:","title":"Estimating Infection Rates from Serological Data","text":"Download Rtools https://cran.r-project.org/bin/windows/Rtools/ Run installer Rtools installation may see window asking “Select Additional Tasks”. select box “Edit system PATH”. devtools RStudio put Rtools PATH automatically needed. select box “Save version information registry”. selected default.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"Estimating Infection Rates from Serological Data","text":"need assistance encounter clear bug, please file issue minimal reproducible example GitHub. Another great resource Epidemiologist R Handbook, includes introductory page asking help R packages via GitHub: https://epirhandbook.com/en/getting-help.html","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/ab.html","id":null,"dir":"Reference","previous_headings":"","what":"kinetics of the antibody (ab) response (power function decay) — ab","title":"kinetics of the antibody (ab) response (power function decay) — ab","text":"kinetics antibody (ab) response (power function decay)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/ab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"kinetics of the antibody (ab) response (power function decay) — ab","text":"","code":"ab(t, par, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/ab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"kinetics of the antibody (ab) response (power function decay) — ab","text":"t age infection? par parameters ... arguments passed baseline()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/ab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"kinetics of the antibody (ab) response (power function decay) — ab","text":"matrix()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.curve_params.html","id":null,"dir":"Reference","previous_headings":"","what":"graph antibody decay curves by antigen isotype — autoplot.curve_params","title":"graph antibody decay curves by antigen isotype — autoplot.curve_params","text":"graph antibody decay curves antigen isotype","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.curve_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"graph antibody decay curves by antigen isotype — autoplot.curve_params","text":"","code":"# S3 method for curve_params autoplot(   object,   antigen_isos = object$antigen_iso %>% unique(),   ncol = min(3, length(antigen_isos)),   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.curve_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"graph antibody decay curves by antigen isotype — autoplot.curve_params","text":"object data.frame() curve parameters (one MCMC samples) antigen_isos antigen isotypes analyze (can used subset curve_params) ncol many columns subfigures use panel plot ... Arguments passed plot_curve_params_one_ab verbose verbose output xlim range x values graph n_curves many curves plot (see details). rows_to_graph rows curve_params plot (overrides n_curves). alpha (passed ggplot2::geom_function()) transparent curves : 0 = fully transparent (invisible) 1 = fully opaque log_x x-axis logarithmic scale (TRUE) linear scale (FALSE, default)?","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.curve_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"graph antibody decay curves by antigen isotype — autoplot.curve_params","text":"ggplot2::ggplot() object","code":""},{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.curve_params.html","id":"rows-to-graph","dir":"Reference","previous_headings":"","what":"rows_to_graph","title":"graph antibody decay curves by antigen isotype — autoplot.curve_params","text":"Note directly specify rows_to_graph calling function, row numbers enumerated separately antigen isotype; words, purposes argument, row numbers start 1 antigen isotype. currently way specify different row numbers different antigen isotypes; want , call plot_curve_params_one_ab() directly antigen isotype combine resulting panels . subset curve_params manually, passing function, set n_curves argument Inf.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.curve_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"graph antibody decay curves by antigen isotype — autoplot.curve_params","text":"","code":"library(dplyr) #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union library(ggplot2)  curve = \"https://osf.io/download/rtw5k/\" %>%   load_curve_params() %>%   autoplot()"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.pop_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot distribution of antibodies — autoplot.pop_data","title":"Plot distribution of antibodies — autoplot.pop_data","text":"autoplot() method pop_data objects","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.pop_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot distribution of antibodies — autoplot.pop_data","text":"","code":"# S3 method for pop_data autoplot(object, log = FALSE, strata = NULL, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.pop_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot distribution of antibodies — autoplot.pop_data","text":"object pop_data object (load_pop_data()) log whether show antibody responses logarithmic scale strata name variable pop_data stratify (NULL stratification) ... unused","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.pop_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot distribution of antibodies — autoplot.pop_data","text":"ggplot2::ggplot object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.pop_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot distribution of antibodies — autoplot.pop_data","text":"","code":"library(dplyr) library(ggplot2)  xs_data <- \"https://osf.io/download//n6cp3/\" %>% load_pop_data() %>% clean_pop_data()  xs_data %>% autoplot(strata = \"Country\")"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot seroincidence.by log-likelihoods — autoplot.seroincidence.by","title":"Plot seroincidence.by log-likelihoods — autoplot.seroincidence.by","text":"Plots log-likelihood curves stratum, seroincidence.objects","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot seroincidence.by log-likelihoods — autoplot.seroincidence.by","text":"","code":"# S3 method for seroincidence.by autoplot(object, ncol = min(3, length(object)), ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot seroincidence.by log-likelihoods — autoplot.seroincidence.by","text":"object '\"seroincidence.\"' object (est.incidence.()) ncol number columns use panel plots ... Arguments passed autoplot.seroincidence log_x x-axis logarithmic scale (TRUE) linear scale (FALSE, default)?","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot seroincidence.by log-likelihoods — autoplot.seroincidence.by","text":"object class \"ggarrange\", ggplot2::ggplot() list() ggplot2::ggplot()s.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot seroincidence.by log-likelihoods — autoplot.seroincidence.by","text":"","code":"library(dplyr) library(ggplot2)  xs_data <- \"https://osf.io/download//n6cp3/\" %>% load_pop_data() %>% clean_pop_data  curve <- \"https://osf.io/download/rtw5k/\" %>% load_curve_params()  noise <-\"https://osf.io/download//hqy4v/\" %>% load_noise_params()  est2 = est.incidence.by( strata = c(\"catchment\"), pop_data = xs_data %>% filter(Country == \"Pakistan\"), curve_params = curve, noise_params = noise %>% filter(Country == \"Pakistan\"), antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"), #num_cores = 8, #Allow for parallel processing to decrease run time build_graph = TRUE ) #> data format is as expected. #> Warning: curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning: noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> data format is as expected. #> data format is as expected.  #plot the log-likelihood curve   autoplot(est2)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the log-likelihood curve for the incidence rate estimate — autoplot.seroincidence","title":"Plot the log-likelihood curve for the incidence rate estimate — autoplot.seroincidence","text":"Plot log-likelihood curve incidence rate estimate","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the log-likelihood curve for the incidence rate estimate — autoplot.seroincidence","text":"","code":"# S3 method for seroincidence autoplot(object, log_x = FALSE, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the log-likelihood curve for the incidence rate estimate — autoplot.seroincidence","text":"object seroincidence object (est.incidence()) log_x x-axis logarithmic scale (TRUE) linear scale (FALSE, default)? ... unused","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the log-likelihood curve for the incidence rate estimate — autoplot.seroincidence","text":"ggplot2::ggplot()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.seroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the log-likelihood curve for the incidence rate estimate — autoplot.seroincidence","text":"","code":"library(dplyr) library(ggplot2)  xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") xs_data = clean_pop_data(xs_data)  curve = load_curve_params(\"https://osf.io/download/rtw5k/\") noise = load_noise_params(\"https://osf.io/download//hqy4v/\")  est1 = est.incidence(   pop_data = xs_data %>% filter(Country == \"Pakistan\"),   curve_param = curve,   noise_param = noise %>% filter(Country == \"Pakistan\"),   antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"),   build_graph = TRUE ) #> data format is as expected.  #plot the log-likelihood curve   autoplot(est1)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.summary.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot method for summary.seroincidence.by objects — autoplot.summary.seroincidence.by","title":"Plot method for summary.seroincidence.by objects — autoplot.summary.seroincidence.by","text":"Plot method summary.seroincidence.objects","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.summary.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot method for summary.seroincidence.by objects — autoplot.summary.seroincidence.by","text":"","code":"# S3 method for summary.seroincidence.by autoplot(object, xvar, alpha = 0.7, shape = 1, width = 0.001, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.summary.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot method for summary.seroincidence.by objects — autoplot.summary.seroincidence.by","text":"object summary.seroincidence.object (generated applying summary() method output est.incidence.()). xvar name stratifying variable object alpha transparency points graph (1 = transparency, 0 = fully transparent) shape shape argument geom_point() width width jitter ... unused","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.summary.seroincidence.by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot method for summary.seroincidence.by objects — autoplot.summary.seroincidence.by","text":"ggplot2::ggplot() object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/autoplot.summary.seroincidence.by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot method for summary.seroincidence.by objects — autoplot.summary.seroincidence.by","text":"","code":"library(dplyr) library(ggplot2)  xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") %>%   clean_pop_data() curve = load_curve_params(\"https://osf.io/download/rtw5k/\" ) noise = load_noise_params(\"https://osf.io/download//hqy4v/\")  est2 = est.incidence.by( strata = c(\"catchment\"), pop_data = xs_data %>% filter(Country == \"Pakistan\"), curve_params = curve, noise_params = noise %>% filter(Country == \"Pakistan\"), antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"), #num_cores = 8 #Allow for parallel processing to decrease run time ) #> data format is as expected. #> Warning: curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning: noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> data format is as expected. #> data format is as expected.  est2sum <- summary(est2)  autoplot(est2sum, \"catchment\")"},{"path":"https://ucd-serg.github.io/serocalculator/reference/check_pop_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Check the formatting of a cross-sectional antibody survey dataset. — check_pop_data","title":"Check the formatting of a cross-sectional antibody survey dataset. — check_pop_data","text":"Check formatting cross-sectional antibody survey dataset.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/check_pop_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check the formatting of a cross-sectional antibody survey dataset. — check_pop_data","text":"","code":"check_pop_data(pop_data)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/check_pop_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check the formatting of a cross-sectional antibody survey dataset. — check_pop_data","text":"pop_data dataset check","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/check_pop_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check the formatting of a cross-sectional antibody survey dataset. — check_pop_data","text":"NULL (invisibly)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/check_pop_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check the formatting of a cross-sectional antibody survey dataset. — check_pop_data","text":"","code":"library(dplyr)  #Import cross-sectional data from OSF and rename required variables xs_data <-  \"https://osf.io/download//n6cp3/\" %>%   load_pop_data() %>%   clean_pop_data()    xs_data %>% check_pop_data() #> data format is as expected."},{"path":"https://ucd-serg.github.io/serocalculator/reference/clean_pop_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Reformat a cross-sectional antibody survey dataset — clean_pop_data","title":"Reformat a cross-sectional antibody survey dataset — clean_pop_data","text":"Reformat cross-sectional antibody survey dataset","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/clean_pop_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reformat a cross-sectional antibody survey dataset — clean_pop_data","text":"","code":"clean_pop_data(pop_data)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/clean_pop_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reformat a cross-sectional antibody survey dataset — clean_pop_data","text":"pop_data data.frame() containing following columns: index_id: character() variable identifying multiple rows data person antigen_isos: character() variable indicating antigen-isotype measured result: measured antibody concentration Age: age individual whose serum assayed, time blood sample collection.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/clean_pop_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reformat a cross-sectional antibody survey dataset — clean_pop_data","text":"data.frame (tibble::tbl_df) containing following columns: id: character() variable identifying multiple rows data person antigen_isos: character() variable indicating antigen-isotype measured value: measured antibody concentration age: age individual whose serum assayed, time blood sample","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/clean_pop_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reformat a cross-sectional antibody survey dataset — clean_pop_data","text":"","code":"xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") clean_pop_data(xs_data)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/df.to.array.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a data.frame (or tibble) into a multidimensional array — df.to.array","title":"Convert a data.frame (or tibble) into a multidimensional array — df.to.array","text":"Convert data.frame (tibble) multidimensional array","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/df.to.array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a data.frame (or tibble) into a multidimensional array — df.to.array","text":"","code":"df.to.array(df, dim_var_names, value_var_name = \"value\")"},{"path":"https://ucd-serg.github.io/serocalculator/reference/df.to.array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a data.frame (or tibble) into a multidimensional array — df.to.array","text":"df data.frame() (tibble::tibble()) long format (row contains one value intended array) dim_var_names character() vector variable names df. variables factors, warning produced. value_var_name character() variable containing variable name df contains values intended array.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/df.to.array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a data.frame (or tibble) into a multidimensional array — df.to.array","text":"array() dimensions defined variables df listed dim_var_names","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/df.to.array.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a data.frame (or tibble) into a multidimensional array — df.to.array","text":"","code":"library(dplyr) library(tidyr) df = iris %>%   tidyr::pivot_longer(   names_to = \"parameter\",   cols = c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Width\", \"Petal.Length\") ) %>%   mutate(parameter = factor(parameter, levels = unique(parameter)))   df %>% serocalculator:::df.to.array(dim_var_names = c(\"parameter\", \"Species\")) #> , , obs = 1 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        7.0       6.3 #>   Sepal.Width     3.5        3.2       3.3 #>   Petal.Width     0.2        1.4       2.5 #>   Petal.Length    1.4        4.7       6.0 #>  #> , , obs = 2 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.9        6.4       5.8 #>   Sepal.Width     3.0        3.2       2.7 #>   Petal.Width     0.2        1.5       1.9 #>   Petal.Length    1.4        4.5       5.1 #>  #> , , obs = 3 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.7        6.9       7.1 #>   Sepal.Width     3.2        3.1       3.0 #>   Petal.Width     0.2        1.5       2.1 #>   Petal.Length    1.3        4.9       5.9 #>  #> , , obs = 4 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.6        5.5       6.3 #>   Sepal.Width     3.1        2.3       2.9 #>   Petal.Width     0.2        1.3       1.8 #>   Petal.Length    1.5        4.0       5.6 #>  #> , , obs = 5 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        6.5       6.5 #>   Sepal.Width     3.6        2.8       3.0 #>   Petal.Width     0.2        1.5       2.2 #>   Petal.Length    1.4        4.6       5.8 #>  #> , , obs = 6 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.4        5.7       7.6 #>   Sepal.Width     3.9        2.8       3.0 #>   Petal.Width     0.4        1.3       2.1 #>   Petal.Length    1.7        4.5       6.6 #>  #> , , obs = 7 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.6        6.3       4.9 #>   Sepal.Width     3.4        3.3       2.5 #>   Petal.Width     0.3        1.6       1.7 #>   Petal.Length    1.4        4.7       4.5 #>  #> , , obs = 8 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        4.9       7.3 #>   Sepal.Width     3.4        2.4       2.9 #>   Petal.Width     0.2        1.0       1.8 #>   Petal.Length    1.5        3.3       6.3 #>  #> , , obs = 9 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.4        6.6       6.7 #>   Sepal.Width     2.9        2.9       2.5 #>   Petal.Width     0.2        1.3       1.8 #>   Petal.Length    1.4        4.6       5.8 #>  #> , , obs = 10 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.9        5.2       7.2 #>   Sepal.Width     3.1        2.7       3.6 #>   Petal.Width     0.1        1.4       2.5 #>   Petal.Length    1.5        3.9       6.1 #>  #> , , obs = 11 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.4        5.0       6.5 #>   Sepal.Width     3.7        2.0       3.2 #>   Petal.Width     0.2        1.0       2.0 #>   Petal.Length    1.5        3.5       5.1 #>  #> , , obs = 12 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.8        5.9       6.4 #>   Sepal.Width     3.4        3.0       2.7 #>   Petal.Width     0.2        1.5       1.9 #>   Petal.Length    1.6        4.2       5.3 #>  #> , , obs = 13 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.8        6.0       6.8 #>   Sepal.Width     3.0        2.2       3.0 #>   Petal.Width     0.1        1.0       2.1 #>   Petal.Length    1.4        4.0       5.5 #>  #> , , obs = 14 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.3        6.1       5.7 #>   Sepal.Width     3.0        2.9       2.5 #>   Petal.Width     0.1        1.4       2.0 #>   Petal.Length    1.1        4.7       5.0 #>  #> , , obs = 15 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.8        5.6       5.8 #>   Sepal.Width     4.0        2.9       2.8 #>   Petal.Width     0.2        1.3       2.4 #>   Petal.Length    1.2        3.6       5.1 #>  #> , , obs = 16 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.7        6.7       6.4 #>   Sepal.Width     4.4        3.1       3.2 #>   Petal.Width     0.4        1.4       2.3 #>   Petal.Length    1.5        4.4       5.3 #>  #> , , obs = 17 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.4        5.6       6.5 #>   Sepal.Width     3.9        3.0       3.0 #>   Petal.Width     0.4        1.5       1.8 #>   Petal.Length    1.3        4.5       5.5 #>  #> , , obs = 18 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        5.8       7.7 #>   Sepal.Width     3.5        2.7       3.8 #>   Petal.Width     0.3        1.0       2.2 #>   Petal.Length    1.4        4.1       6.7 #>  #> , , obs = 19 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.7        6.2       7.7 #>   Sepal.Width     3.8        2.2       2.6 #>   Petal.Width     0.3        1.5       2.3 #>   Petal.Length    1.7        4.5       6.9 #>  #> , , obs = 20 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        5.6       6.0 #>   Sepal.Width     3.8        2.5       2.2 #>   Petal.Width     0.3        1.1       1.5 #>   Petal.Length    1.5        3.9       5.0 #>  #> , , obs = 21 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.4        5.9       6.9 #>   Sepal.Width     3.4        3.2       3.2 #>   Petal.Width     0.2        1.8       2.3 #>   Petal.Length    1.7        4.8       5.7 #>  #> , , obs = 22 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        6.1       5.6 #>   Sepal.Width     3.7        2.8       2.8 #>   Petal.Width     0.4        1.3       2.0 #>   Petal.Length    1.5        4.0       4.9 #>  #> , , obs = 23 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.6        6.3       7.7 #>   Sepal.Width     3.6        2.5       2.8 #>   Petal.Width     0.2        1.5       2.0 #>   Petal.Length    1.0        4.9       6.7 #>  #> , , obs = 24 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        6.1       6.3 #>   Sepal.Width     3.3        2.8       2.7 #>   Petal.Width     0.5        1.2       1.8 #>   Petal.Length    1.7        4.7       4.9 #>  #> , , obs = 25 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.8        6.4       6.7 #>   Sepal.Width     3.4        2.9       3.3 #>   Petal.Width     0.2        1.3       2.1 #>   Petal.Length    1.9        4.3       5.7 #>  #> , , obs = 26 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        6.6       7.2 #>   Sepal.Width     3.0        3.0       3.2 #>   Petal.Width     0.2        1.4       1.8 #>   Petal.Length    1.6        4.4       6.0 #>  #> , , obs = 27 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        6.8       6.2 #>   Sepal.Width     3.4        2.8       2.8 #>   Petal.Width     0.4        1.4       1.8 #>   Petal.Length    1.6        4.8       4.8 #>  #> , , obs = 28 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.2        6.7       6.1 #>   Sepal.Width     3.5        3.0       3.0 #>   Petal.Width     0.2        1.7       1.8 #>   Petal.Length    1.5        5.0       4.9 #>  #> , , obs = 29 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.2        6.0       6.4 #>   Sepal.Width     3.4        2.9       2.8 #>   Petal.Width     0.2        1.5       2.1 #>   Petal.Length    1.4        4.5       5.6 #>  #> , , obs = 30 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.7        5.7       7.2 #>   Sepal.Width     3.2        2.6       3.0 #>   Petal.Width     0.2        1.0       1.6 #>   Petal.Length    1.6        3.5       5.8 #>  #> , , obs = 31 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.8        5.5       7.4 #>   Sepal.Width     3.1        2.4       2.8 #>   Petal.Width     0.2        1.1       1.9 #>   Petal.Length    1.6        3.8       6.1 #>  #> , , obs = 32 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.4        5.5       7.9 #>   Sepal.Width     3.4        2.4       3.8 #>   Petal.Width     0.4        1.0       2.0 #>   Petal.Length    1.5        3.7       6.4 #>  #> , , obs = 33 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.2        5.8       6.4 #>   Sepal.Width     4.1        2.7       2.8 #>   Petal.Width     0.1        1.2       2.2 #>   Petal.Length    1.5        3.9       5.6 #>  #> , , obs = 34 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.5        6.0       6.3 #>   Sepal.Width     4.2        2.7       2.8 #>   Petal.Width     0.2        1.6       1.5 #>   Petal.Length    1.4        5.1       5.1 #>  #> , , obs = 35 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.9        5.4       6.1 #>   Sepal.Width     3.1        3.0       2.6 #>   Petal.Width     0.2        1.5       1.4 #>   Petal.Length    1.5        4.5       5.6 #>  #> , , obs = 36 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        6.0       7.7 #>   Sepal.Width     3.2        3.4       3.0 #>   Petal.Width     0.2        1.6       2.3 #>   Petal.Length    1.2        4.5       6.1 #>  #> , , obs = 37 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.5        6.7       6.3 #>   Sepal.Width     3.5        3.1       3.4 #>   Petal.Width     0.2        1.5       2.4 #>   Petal.Length    1.3        4.7       5.6 #>  #> , , obs = 38 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.9        6.3       6.4 #>   Sepal.Width     3.6        2.3       3.1 #>   Petal.Width     0.1        1.3       1.8 #>   Petal.Length    1.4        4.4       5.5 #>  #> , , obs = 39 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.4        5.6       6.0 #>   Sepal.Width     3.0        3.0       3.0 #>   Petal.Width     0.2        1.3       1.8 #>   Petal.Length    1.3        4.1       4.8 #>  #> , , obs = 40 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        5.5       6.9 #>   Sepal.Width     3.4        2.5       3.1 #>   Petal.Width     0.2        1.3       2.1 #>   Petal.Length    1.5        4.0       5.4 #>  #> , , obs = 41 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        5.5       6.7 #>   Sepal.Width     3.5        2.6       3.1 #>   Petal.Width     0.3        1.2       2.4 #>   Petal.Length    1.3        4.4       5.6 #>  #> , , obs = 42 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.5        6.1       6.9 #>   Sepal.Width     2.3        3.0       3.1 #>   Petal.Width     0.3        1.4       2.3 #>   Petal.Length    1.3        4.6       5.1 #>  #> , , obs = 43 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.4        5.8       5.8 #>   Sepal.Width     3.2        2.6       2.7 #>   Petal.Width     0.2        1.2       1.9 #>   Petal.Length    1.3        4.0       5.1 #>  #> , , obs = 44 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        5.0       6.8 #>   Sepal.Width     3.5        2.3       3.2 #>   Petal.Width     0.6        1.0       2.3 #>   Petal.Length    1.6        3.3       5.9 #>  #> , , obs = 45 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        5.6       6.7 #>   Sepal.Width     3.8        2.7       3.3 #>   Petal.Width     0.4        1.3       2.5 #>   Petal.Length    1.9        4.2       5.7 #>  #> , , obs = 46 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.8        5.7       6.7 #>   Sepal.Width     3.0        3.0       3.0 #>   Petal.Width     0.3        1.2       2.3 #>   Petal.Length    1.4        4.2       5.2 #>  #> , , obs = 47 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.1        5.7       6.3 #>   Sepal.Width     3.8        2.9       2.5 #>   Petal.Width     0.2        1.3       1.9 #>   Petal.Length    1.6        4.2       5.0 #>  #> , , obs = 48 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    4.6        6.2       6.5 #>   Sepal.Width     3.2        2.9       3.0 #>   Petal.Width     0.2        1.3       2.0 #>   Petal.Length    1.4        4.3       5.2 #>  #> , , obs = 49 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.3        5.1       6.2 #>   Sepal.Width     3.7        2.5       3.4 #>   Petal.Width     0.2        1.1       2.3 #>   Petal.Length    1.5        3.0       5.4 #>  #> , , obs = 50 #>  #>               Species #> parameter      setosa versicolor virginica #>   Sepal.Length    5.0        5.7       5.9 #>   Sepal.Width     3.3        2.8       3.0 #>   Petal.Width     0.2        1.3       1.8 #>   Petal.Length    1.4        4.1       5.1 #>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate negative log-likelihood — .nll","title":"Calculate negative log-likelihood — .nll","text":"llik(), except negated requiring lambda log scale (used combination nlm(), ensure optimization search stray negative values lambda).","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate negative log-likelihood — .nll","text":"","code":".nll(log.lambda, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate negative log-likelihood — .nll","text":"log.lambda natural logarithm incidence rate ... Arguments passed llik pop_data data.frame() cross-sectional serology data per antibody age, additional columns antigen_isos Character vector listing one antigen isotypes. Values must match pop_data. curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay noise_params data.frame() (tibble::tibble()) containing following variables, specifying noise parameters antigen isotype: antigen_iso: antigen isotype whose noise parameters specified row nu: biological noise eps: measurement noise y.low: lower limit detection current antigen isotype y.high: upper limit detection current antigen isotype verbose logical: TRUE, print verbose log information console","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/dot-nll.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate negative log-likelihood — .nll","text":"negative log-likelihood data current parameter values","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate Seroincidence — est.incidence.by","title":"Estimate Seroincidence — est.incidence.by","text":"Function estimate seroincidences based cross-section serology data longitudinal response model.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate Seroincidence — est.incidence.by","text":"","code":"est.incidence.by(   pop_data,   curve_params,   noise_params,   strata,   curve_strata_varnames = strata,   noise_strata_varnames = strata,   antigen_isos = pop_data %>% pull(\"antigen_iso\") %>% unique(),   lambda_start = 0.1,   build_graph = FALSE,   num_cores = 1L,   verbose = FALSE,   print_graph = FALSE,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate Seroincidence — est.incidence.by","text":"pop_data data.frame() cross-sectional serology data per antibody age, additional columns identify possible strata. curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay noise_params data.frame() (tibble::tibble()) containing following variables, specifying noise parameters antigen isotype: antigen_iso: antigen isotype whose noise parameters specified row nu: biological noise eps: measurement noise y.low: lower limit detection current antigen isotype y.high: upper limit detection current antigen isotype strata Character vector stratum-defining variables. Values must variable names pop_data. curve_strata_varnames subset strata. Values must variable names curve_params. Default = \"\". noise_strata_varnames subset strata. Values must variable names noise_params. Default = \"\". antigen_isos Character vector one antibody names. Values must match pop_data lambda_start starting guess incidence rate, years/event. build_graph whether graph log-likelihood function across range incidence rates (lambda values) num_cores Number processor cores use calculations computing strata. set 1 package parallel available, computations executed parallel. Default = 1L. verbose logical: TRUE, print verbose log information console print_graph whether display log-likelihood curve graph course running est.incidence() ... Arguments passed est.incidence, stats::nlm stepmin positive scalar providing minimum allowable relative step length. stepmax positive scalar gives maximum allowable     scaled step length.  stepmax used prevent steps     cause optimization function overflow, prevent     algorithm leaving area interest parameter space,     detect divergence algorithm. stepmax chosen     small enough prevent first two occurrences,     larger anticipated reasonable step. typsize estimate size parameter     minimum. fscale estimate size f minimum. ndigit number significant digits function f. gradtol positive scalar giving tolerance     scaled gradient considered close enough zero     terminate algorithm.  scaled gradient     measure relative change f direction     p[] divided relative change p[]. iterlim positive integer specifying maximum number     iterations performed program terminated. check.analyticals logical scalar specifying whether     analytic gradients Hessians, supplied,     checked numerical derivatives initial parameter     values. can help detect incorrectly formulated gradients     Hessians.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate Seroincidence — est.incidence.by","text":"strata meaningful inputs: object class \"seroincidence.\"; .e., list \"seroincidence\" objects est.incidence(), one stratum, meta-data attributes. strata missing, NULL, NA, \"\": object class \"seroincidence\".","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.by.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate Seroincidence — est.incidence.by","text":"strata left empty, warning produced, recommending use est.incidence() unstratified analyses, data passed est.incidence(). reason want use est.incidence.() strata instead calling est.incidence(), may use NA, NULL, \"\" strata argument avoid warning.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate Seroincidence — est.incidence.by","text":"","code":"library(dplyr)  xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") %>%   clean_pop_data() curve = load_curve_params(\"https://osf.io/download/rtw5k/\") noise = load_noise_params(\"https://osf.io/download//hqy4v/\")  est2 = est.incidence.by( strata = c(\"catchment\"), pop_data = xs_data %>% filter(Country == \"Pakistan\"), curve_params = curve, noise_params = noise %>% filter(Country == \"Pakistan\"), antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"), #num_cores = 8 #Allow for parallel processing to decrease run time ) #> data format is as expected. #> Warning: curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning: noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> data format is as expected. #> data format is as expected.  summary(est2) #> Seroincidence estimated given the following setup: #> a) Antigen isotypes   : HlyE_IgG, HlyE_IgA  #> b) Strata       : catchment  #>  #>  Seroincidence estimates: #> # A tibble: 2 × 13 #>   Stratum   catchment     n est.start incidence.rate      SE CI.lwr CI.upr #>   <chr>     <chr>     <int>     <dbl>          <dbl>   <dbl>  <dbl>  <dbl> #> 1 Stratum 1 aku         294       0.1          0.106 0.00767 0.0916  0.122 #> 2 Stratum 2 kgh         200       0.1          0.167 0.0133  0.143   0.196 #> # ℹ 5 more variables: coverage <dbl>, log.lik <dbl>, iterations <int>, #> #   antigen.isos <chr>, nlm.convergence.code <ord>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the maximum likelihood estimate of the incidence rate parameter — est.incidence","title":"Find the maximum likelihood estimate of the incidence rate parameter — est.incidence","text":"function models seroincidence using maximum likelihood estimation; , finds value seroincidence parameter maximizes likelihood (.e., joint probability) data.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the maximum likelihood estimate of the incidence rate parameter — est.incidence","text":"","code":"est.incidence(   pop_data,   curve_params,   noise_params,   antigen_isos = pop_data$antigen_iso %>% unique(),   lambda_start = 0.1,   stepmin = 1e-08,   stepmax = 3,   verbose = FALSE,   build_graph = FALSE,   print_graph = build_graph & verbose,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the maximum likelihood estimate of the incidence rate parameter — est.incidence","text":"pop_data data.frame() cross-sectional serology data per antibody age, additional columns curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay noise_params data.frame() (tibble::tibble()) containing following variables, specifying noise parameters antigen isotype: antigen_iso: antigen isotype whose noise parameters specified row nu: biological noise eps: measurement noise y.low: lower limit detection current antigen isotype y.high: upper limit detection current antigen isotype antigen_isos Character vector one antibody names. Values must match pop_data lambda_start starting guess incidence rate, years/event. stepmin positive scalar providing minimum allowable relative step length. stepmax positive scalar gives maximum allowable     scaled step length.  stepmax used prevent steps     cause optimization function overflow, prevent     algorithm leaving area interest parameter space,     detect divergence algorithm. stepmax chosen     small enough prevent first two occurrences,     larger anticipated reasonable step. verbose logical: TRUE, print verbose log information console build_graph whether graph log-likelihood function across range incidence rates (lambda values) print_graph whether display log-likelihood curve graph course running est.incidence() ... Arguments passed stats::nlm typsize estimate size parameter     minimum. fscale estimate size f minimum. ndigit number significant digits function f. gradtol positive scalar giving tolerance     scaled gradient considered close enough zero     terminate algorithm.  scaled gradient     measure relative change f direction     p[] divided relative change p[]. iterlim positive integer specifying maximum number     iterations performed program terminated. check.analyticals logical scalar specifying whether     analytic gradients Hessians, supplied,     checked numerical derivatives initial parameter     values. can help detect incorrectly formulated gradients     Hessians.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the maximum likelihood estimate of the incidence rate parameter — est.incidence","text":"\"seroincidence\" object, stats::nlm() fit object extra meta-data attributes lambda_start, antigen_isos, ll_graph","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/est.incidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find the maximum likelihood estimate of the incidence rate parameter — est.incidence","text":"","code":"library(dplyr)  xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") %>% clean_pop_data() curve = load_curve_params(\"https://osf.io/download/rtw5k/\") noise = load_noise_params(\"https://osf.io/download//hqy4v/\")  est1 = est.incidence( pop_data = xs_data %>% filter(Country == \"Pakistan\"), curve_param = curve, noise_param = noise %>% filter(Country == \"Pakistan\"), antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\") ) #> data format is as expected.  summary(est1) #> # A tibble: 1 × 10 #>   est.start incidence.rate      SE CI.lwr CI.upr coverage log.lik iterations #>       <dbl>          <dbl>   <dbl>  <dbl>  <dbl>    <dbl>   <dbl>      <int> #> 1       0.1          0.128 0.00682  0.115  0.142     0.95  -2376.          4 #> # ℹ 2 more variables: antigen.isos <chr>, nlm.convergence.code <ord>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/fdev.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate negative log-likelihood (deviance) — fdev","title":"Calculate negative log-likelihood (deviance) — fdev","text":"description added ","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/fdev.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate negative log-likelihood (deviance) — fdev","text":"","code":"fdev(lambda, csdata, lnpars, cond)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/fdev.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate negative log-likelihood (deviance) — fdev","text":"lambda incidence parameter, events per person-year csdata cross-sectional sample data containing variables value age lnpars longitudinal antibody decay model parameters alpha, y1, d cond measurement noise parameters nu, eps, y.low, y.high","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Additional Data — getAdditionalData","title":"Get Additional Data — getAdditionalData","text":"Retrieves additional data internet. data format must .RDS zipped .RDS. purpose function download data longitudinal response parameters online repository cross-sectional population data. Data package available : https://osf.io/ne8pc/files/osfstorage can save data chosen directory using optional savePath argument. Specify file path file name. Large datasets may timeout. , can increase download time updating maximum timeout time code . (Ex: increase 300 1000) options(timeout = max(300, getOption(\"timeout\")))","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Additional Data — getAdditionalData","text":"","code":"getAdditionalData(fileURL, savePath = NULL)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Additional Data — getAdditionalData","text":"fileURL URL file downloaded. savePath Folder directory filename save downloaded unzipped (needed) file. File saved argument NULL. Optional. Default = NULL.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Additional Data — getAdditionalData","text":"R object stored file indicated fileURL input Data object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/getAdditionalData.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Additional Data — getAdditionalData","text":"","code":"if (FALSE) { curve_param_samples =   getAdditionalData(     fileURL = \"https://osf.io/download/bhfvx\")  # optionally, save the data to disk curve_param_samples =   getAdditionalData(     fileURL = \"https://osf.io/download/bhfvx\",     savePath = \"~/Downloads/curv_params.rds\") }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.curve.params.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph estimated antibody decay curve — graph.curve.params","title":"Graph estimated antibody decay curve — graph.curve.params","text":"Graph estimated antibody decay curve","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.curve.params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph estimated antibody decay curve — graph.curve.params","text":"","code":"graph.curve.params(   curve_params,   antigen_isos = unique(curve_params$antigen_iso),   verbose = FALSE )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.curve.params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph estimated antibody decay curve — graph.curve.params","text":"curve_params data.frame() containing MCMC samples antibody decay curve parameters antigen_isos antigen isotypes verbose verbose output","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.curve.params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph estimated antibody decay curve — graph.curve.params","text":"ggplot2::ggplot() object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.curve.params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph estimated antibody decay curve — graph.curve.params","text":"","code":"if (FALSE) { curve_params = readRDS(url(\"https://osf.io/download/rtw5k/\")) plot1 = graph.curve.params(curve_params) print(plot1) }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.loglik.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph log-likelihood of data — graph.loglik","title":"Graph log-likelihood of data — graph.loglik","text":"Graph log-likelihood data","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.loglik.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph log-likelihood of data — graph.loglik","text":"","code":"graph.loglik(   pop_data,   curve_params,   noise_params,   antigen_isos,   x = 10^seq(-3, 0, by = 0.1),   highlight_points = NULL,   highlight_point_names = \"highlight_points\",   log_x = FALSE,   previous_plot = NULL,   curve_label = paste(antigen_isos, collapse = \" + \"),   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.loglik.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph log-likelihood of data — graph.loglik","text":"pop_data data.frame() cross-sectional serology data per antibody age, additional columns curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay noise_params data.frame() (tibble::tibble()) containing following variables, specifying noise parameters antigen isotype: antigen_iso: antigen isotype whose noise parameters specified row nu: biological noise eps: measurement noise y.low: lower limit detection current antigen isotype y.high: upper limit detection current antigen isotype antigen_isos Character vector listing one antigen isotypes. Values must match pop_data. x sequence lambda values graph highlight_points possible highlighted value highlight_point_names labels highlighted points log_x x-axis logarithmic scale (TRUE) linear scale (FALSE, default)? previous_plot NULL, current data added existing graph curve_label NULL, add label curve ... Arguments passed llik verbose logical: TRUE, print verbose log information console","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.loglik.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph log-likelihood of data — graph.loglik","text":"ggplot2::ggplot()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/graph.loglik.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph log-likelihood of data — graph.loglik","text":"","code":"library(dplyr) library(tibble)  #Load cross-sectional data xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") %>% clean_pop_data()   #Load curve parameters dmcmc = load_curve_params(\"https://osf.io/download/rtw5k\" )  #Load noise parameters cond <- tibble( antigen_iso = c(\"HlyE_IgG\", \"HlyE_IgA\"), nu = c(0.5, 0.5),                          # Biologic noise (nu) eps = c(0, 0),                             # M noise (eps) y.low = c(1, 1),                           # low cutoff (llod) y.high = c(5e6, 5e6))                      # high cutoff (y.high)  #Graph the log likelihood lik_HlyE_IgA = graph.loglik(  pop_data = xs_data,  curve_params = dmcmc,  noise_params = cond,  antigen_isos = \"HlyE_IgA\",  log_x = TRUE )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/ldpar.html","id":null,"dir":"Reference","previous_headings":"","what":"extract a row from longitudinal parameter set — ldpar","title":"extract a row from longitudinal parameter set — ldpar","text":"take random sample longitudinal parameter set given age infection,  list antibodies","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/ldpar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"extract a row from longitudinal parameter set — ldpar","text":"","code":"ldpar(age, antigen_isos, nmc, npar, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/ldpar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"extract a row from longitudinal parameter set — ldpar","text":"age age infection antigen_isos antigen isotypes nmc mcmc sample use npar number parameters ... passed simpar()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/ldpar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"extract a row from longitudinal parameter set — ldpar","text":"array parameters: c(y0,b0,mu0,mu1,c1,alpha,shape)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/llik.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate log-likelihood — llik","title":"Calculate log-likelihood — llik","text":"Calculates log-likelihood set cross-sectional antibody response data, given incidence rate (lambda) value.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/llik.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate log-likelihood — llik","text":"","code":"llik(   lambda,   pop_data,   antigen_isos,   curve_params,   noise_params,   verbose = FALSE,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/llik.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate log-likelihood — llik","text":"lambda incidence parameter, events per person-year pop_data data.frame() cross-sectional serology data per antibody age, additional columns antigen_isos Character vector listing one antigen isotypes. Values must match pop_data. curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay noise_params data.frame() (tibble::tibble()) containing following variables, specifying noise parameters antigen isotype: antigen_iso: antigen isotype whose noise parameters specified row nu: biological noise eps: measurement noise y.low: lower limit detection current antigen isotype y.high: upper limit detection current antigen isotype verbose logical: TRUE, print verbose log information console ... additional arguments passed functions (currently used).","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/llik.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate log-likelihood — llik","text":"log-likelihood data current parameter values","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/llik.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate log-likelihood — llik","text":"","code":"library(dplyr) library(tibble)  #load in longitudinal parameters dmcmc = load_curve_params(\"https://osf.io/download/rtw5k\")  xs_data <- \"https://osf.io/download//n6cp3/\" %>% load_pop_data() %>% clean_pop_data()  #Load noise params   cond <- tibble(   antigen_iso = c(\"HlyE_IgG\", \"HlyE_IgA\"),   nu = c(0.5, 0.5),                          # Biologic noise (nu)   eps = c(0, 0),                             # M noise (eps)   y.low = c(1, 1),                           # low cutoff (llod)   y.high = c(5e6, 5e6))                      # high cutoff (y.high)  #Calculate log-likelihood   ll_AG = llik(   pop_data = xs_data,   curve_params = dmcmc,   noise_params = cond,   antigen_isos = c(\"HlyE_IgG\",\"HlyE_IgA\"),   lambda = 0.1) %>% print() #> [1] -9268.824"},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_curve_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Load antibody decay curve parameter samples — load_curve_params","title":"Load antibody decay curve parameter samples — load_curve_params","text":"Load antibody decay curve parameter samples","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_curve_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load antibody decay curve parameter samples — load_curve_params","text":"","code":"load_curve_params(file_path, antigen_isos = NULL)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_curve_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load antibody decay curve parameter samples — load_curve_params","text":"file_path path RDS file containing MCMC samples antibody decay curve parameters y0, y1, t1, alpha, r, stored data.frame() tibble::tbl_df antigen_isos character() vector antigen isotypes used analyses","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_curve_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load antibody decay curve parameter samples — load_curve_params","text":"curve_params object (tibble::tbl_df extra attribute antigen_isos)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_curve_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load antibody decay curve parameter samples — load_curve_params","text":"","code":"curve = load_curve_params(\"https://osf.io/download/rtw5k/\" ) print(curve) #> # A tibble: 20,000 × 7 #>    antigen_iso  iter     y0     y1    t1     alpha     r #>    <fct>       <int>  <dbl>  <dbl> <dbl>     <dbl> <dbl> #>  1 HlyE_IgA        1  2.48    63.5  9.52 0.000581   1.75 #>  2 HlyE_IgG        1  3.04   164.   6.55 0.00457    1.17 #>  3 LPS_IgA         1  0.748  103.   4.98 0.00308    1.58 #>  4 LPS_IgG         1  0.941  320.   6.14 0.00166    1.41 #>  5 Vi_IgG          1  8.46  4348.   3.07 0.0000340  1.06 #>  6 HlyE_IgA        2  3.86   288.   1.27 0.000459   2.66 #>  7 HlyE_IgG        2  1.82   154.  10.8  0.000921   1.30 #>  8 LPS_IgA         2  1.76   852.   2.49 0.000126   2.91 #>  9 LPS_IgG         2  0.434   20.6  4.29 0.00122    1.37 #> 10 Vi_IgG          2 18.8    345.   3.48 0.000142   1.02 #> # ℹ 19,990 more rows"},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_noise_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Load noise parameters — load_noise_params","title":"Load noise parameters — load_noise_params","text":"Load noise parameters","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_noise_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load noise parameters — load_noise_params","text":"","code":"load_noise_params(file_path, antigen_isos = NULL)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_noise_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load noise parameters — load_noise_params","text":"file_path path RDS file containing biologic measurement noise antibody decay curve parameters y.low, eps, nu, y.high, stored data.frame() tibble::tbl_df antigen_isos character() vector antigen isotypes used analyses","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_noise_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load noise parameters — load_noise_params","text":"noise object (tibble::tbl_df extra attribute antigen_isos)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_noise_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load noise parameters — load_noise_params","text":"","code":"noise = load_noise_params(\"https://osf.io/download//hqy4v/\") print(noise) #> # A tibble: 16 × 6 #>    antigen_iso Country    y.low   eps    nu  y.high #>    <chr>       <fct>      <dbl> <dbl> <dbl>   <dbl> #>  1 HlyE_IgA    Bangladesh 0.376 0.280  2.60 5000000 #>  2 HlyE_IgA    Ghana      0.179 0.240  2.60 5000000 #>  3 HlyE_IgA    Nepal      0.853 0.238  2.60 5000000 #>  4 HlyE_IgA    Pakistan   0.508 0.279  2.60 5000000 #>  5 HlyE_IgG    Bangladesh 0.787 0.306  2.36 5000000 #>  6 HlyE_IgG    Ghana      0.645 0.164  2.36 5000000 #>  7 HlyE_IgG    Nepal      1.89  0.128  2.36 5000000 #>  8 HlyE_IgG    Pakistan   1.59  0.146  2.36 5000000 #>  9 LPS_IgA     Bangladesh 0.660 0.299  2.14 5000000 #> 10 LPS_IgA     Ghana      0.861 0.163  2.14 5000000 #> 11 LPS_IgA     Nepal      1.79  0.115  2.14 5000000 #> 12 LPS_IgA     Pakistan   5.13  0.246  2.14 5000000 #> 13 LPS_IgG     Bangladesh 0.992 0.298  3.24 5000000 #> 14 LPS_IgG     Ghana      0.885 0.195  3.24 5000000 #> 15 LPS_IgG     Nepal      0.647 0.179  3.24 5000000 #> 16 LPS_IgG     Pakistan   4.84  0.273  3.24 5000000"},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_pop_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Load a cross-sectional antibody survey data set — load_pop_data","title":"Load a cross-sectional antibody survey data set — load_pop_data","text":"Load cross-sectional antibody survey data set","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_pop_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load a cross-sectional antibody survey data set — load_pop_data","text":"","code":"load_pop_data(file_path, antigen_isos = NULL)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_pop_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load a cross-sectional antibody survey data set — load_pop_data","text":"file_path path RDS file containing cross-sectional antibody survey data set, stored data.frame() tibble::tbl_df antigen_isos character() vector antigen isotypes used analyses","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_pop_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load a cross-sectional antibody survey data set — load_pop_data","text":"pop_data object (tibble::tbl_df extra attribute antigen_isos)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/load_pop_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load a cross-sectional antibody survey data set — load_pop_data","text":"","code":"xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") print(xs_data) #> # A tibble: 3,336 × 8 #>    index_id Country    cluster  catchment   Age ageCat antigen_iso result #>    <chr>    <chr>      <fct>    <chr>     <dbl> <fct>  <fct>        <dbl> #>  1 B1       Bangladesh Ward 2 N dhaka       4   <5     HlyE_IgA     7.24  #>  2 B2       Bangladesh Ward 2 N dhaka       4   <5     HlyE_IgG    24.0   #>  3 B3       Bangladesh Ward 2 N dhaka       3.7 <5     HlyE_IgA     0.836 #>  4 B4       Bangladesh Ward 2 N dhaka       3.7 <5     HlyE_IgG     3.17  #>  5 B5       Bangladesh Ward 2 N dhaka      10.6 5-15   HlyE_IgA     3.42  #>  6 B6       Bangladesh Ward 2 N dhaka      10.6 5-15   HlyE_IgG    14.3   #>  7 B7       Bangladesh Ward 2 N dhaka      16   16+    HlyE_IgA     8.84  #>  8 B8       Bangladesh Ward 2 N dhaka      16   16+    HlyE_IgG    14.2   #>  9 B9       Bangladesh Ward 2 N dhaka      16.5 16+    HlyE_IgA    10.3   #> 10 B10      Bangladesh Ward 2 N dhaka      16.5 16+    HlyE_IgG    29.4   #> # ℹ 3,326 more rows"},{"path":"https://ucd-serg.github.io/serocalculator/reference/mkbaseline.html","id":null,"dir":"Reference","previous_headings":"","what":"generate random sample from baseline distribution — mkbaseline","title":"generate random sample from baseline distribution — mkbaseline","text":"generate random sample baseline distribution","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/mkbaseline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generate random sample from baseline distribution — mkbaseline","text":"","code":"mkbaseline(kab, n = 1, blims, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/mkbaseline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generate random sample from baseline distribution — mkbaseline","text":"kab index row antibody baseline limits read blims n number observations blims range possible baseline antibody levels ... currently used","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/mkbaseline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"generate random sample from baseline distribution — mkbaseline","text":"numeric() vector","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/plot_curve_params_one_ab.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph an antibody decay curve model — plot_curve_params_one_ab","title":"Graph an antibody decay curve model — plot_curve_params_one_ab","text":"Graph antibody decay curve model","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/plot_curve_params_one_ab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph an antibody decay curve model — plot_curve_params_one_ab","text":"","code":"plot_curve_params_one_ab(   object,   verbose = FALSE,   alpha = 0.4,   n_curves = 100,   log_x = FALSE,   rows_to_graph = 1:min(n_curves, nrow(object)),   xlim = c(10^-1, 10^3.1),   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/plot_curve_params_one_ab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph an antibody decay curve model — plot_curve_params_one_ab","text":"object data.frame() curve parameters (one MCMC samples) verbose verbose output alpha (passed ggplot2::geom_function()) transparent curves : 0 = fully transparent (invisible) 1 = fully opaque n_curves many curves plot (see details). log_x x-axis logarithmic scale (TRUE) linear scale (FALSE, default)? rows_to_graph rows curve_params plot (overrides n_curves). xlim range x values graph ... arguments passed layer(). often aesthetics, used set aesthetic fixed value, like colour = \"red\" size = 3. may also parameters paired geom/stat.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/plot_curve_params_one_ab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph an antibody decay curve model — plot_curve_params_one_ab","text":"ggplot2::ggplot() object","code":""},{"path":[]},{"path":"https://ucd-serg.github.io/serocalculator/reference/plot_curve_params_one_ab.html","id":"n-curves-and-rows-to-graph","dir":"Reference","previous_headings":"","what":"n_curves and rows_to_graph","title":"Graph an antibody decay curve model — plot_curve_params_one_ab","text":"cases, curve_params contain many rows MCMC samples samples plotted . Setting  n_curves argument value smaller number rows curve_params cause function select first n_curves rows graph. Setting n_curves larger number rows ` result curves plotted. user directly specifies rows_to_graph argument, n_curves effect.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/plot_curve_params_one_ab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph an antibody decay curve model — plot_curve_params_one_ab","text":"","code":"library(dplyr) # loads the `%>%` operator and `dplyr::filter()` \"https://osf.io/download/rtw5k/\" %>% load_curve_params() %>% dplyr::filter(antigen_iso == \"HlyE_IgG\") %>% serocalculator:::plot_curve_params_one_ab()"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Method for seroincidence.by Object — print.seroincidence.by","title":"Print Method for seroincidence.by Object — print.seroincidence.by","text":"Custom print() function show output seroincidence calculator est.incidence.().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Method for seroincidence.by Object — print.seroincidence.by","text":"","code":"# S3 method for seroincidence.by print(x, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Method for seroincidence.by Object — print.seroincidence.by","text":"x list containing output function est.incidence.(). ... Additional arguments affecting summary produced.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Method for seroincidence.by Object — print.seroincidence.by","text":"","code":"if (FALSE) { # estimate seroincidence seroincidence <- est.incidence.by(...)  # print the seroincidence object to the console print(seroincidence)  # or simply type (appropriate print method will be invoked automatically) seroincidence }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Method for seroincidence Object — print.seroincidence","title":"Print Method for seroincidence Object — print.seroincidence","text":"Custom print() function show output seroincidence calculator est.incidence().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Method for seroincidence Object — print.seroincidence","text":"","code":"# S3 method for seroincidence print(x, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Method for seroincidence Object — print.seroincidence","text":"x list containing output function est.incidence.(). ... Additional arguments affecting summary produced.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.seroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Method for seroincidence Object — print.seroincidence","text":"","code":"if (FALSE) { # estimate seroincidence seroincidence <- est.incidence.by(...)  # print the seroincidence object to the console print(seroincidence)  # or simply type (appropriate print method will be invoked automatically) seroincidence }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Method for Seroincidence Summary Object — print.summary.seroincidence.by","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence.by","text":"Custom print() function \"summary.seroincidence.\" objects (constructed summary.seroincidence.())","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence.by","text":"","code":"# S3 method for summary.seroincidence.by print(x, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence.by","text":"x \"summary.seroincidence.\" object (constructed summary.seroincidence.()) ... Additional arguments affecting summary produced.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/print.summary.seroincidence.by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Method for Seroincidence Summary Object — print.summary.seroincidence.by","text":"","code":"if (FALSE) { # estimate seroincidence seroincidence <- est.incidence.by(...)  # calculate summary statistics for the seroincidence object seroincidenceSummary <- summary(seroincidence)  # print the summary of seroincidence object to the console print(seroincidenceSummary)  # or simply type (appropriate print method will be invoked automatically) seroincidenceSummary }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimating Infection Rates from Serological Data — serocalculator","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"package translates antibody levels measured (cross-sectional) population sample estimate frequency seroconversions (infections) occur sampled population. API package includes following functions:","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"_PACKAGE","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"Methods estimating seroincidence Teunis, P. F. M., J. C. H. van Eijkeren. \"Estimation seroconversion rates infectious diseases: Effects age noise.\" Statistics Medicine 39, . 21 (2020): 2799-2814. Teunis, P. F. M., J. C. H. van Eijkeren, W. F. de Graaf, . Bonačić Marinović, M. E. E. Kretzschmar. \"Linking seroresponse infection within-host heterogeneity antibody production.\" Epidemics 16 (2016): 33-39. Applications Aiemjoy, Kristen, Jessica C. Seidman, Senjuti Saha, Sira Jam Munira, Mohammad Saiful Islam Sajib, Syed Muktadir Al Sium, Anik Sarkar et al. \"Estimating typhoid incidence community-based serosurveys: multicohort study.\" Lancet Microbe 3, . 8 (2022): e578-e587. Aiemjoy, Kristen, John Rumunu, Juma John Hassen, Kirsten E. Wiens, Denise Garrett, Polina Kamenskaya, Jason B. Harris et al. \"Seroincidence enteric fever, Juba, South Sudan.\" Emerging infectious diseases 28, . 11 (2022): 2316. Monge, S., Teunis, P. F., Friesema, ., Franz, E., Ang, W., van Pelt, W., Mughini-Gras, L. \"Immune response-eliciting exposure Campylobacter vastly exceeds incidence clinically overt campylobacteriosis associated similar risk factors: nationwide serosurvey Netherlands\" Journal Infection, 2018, 1--7, doi:10.1016/j.jinf.2018.04.016 Kretzschmar, M., Teunis, P. F., Pebody, R. G. \"Incidence reproduction numbers pertussis: estimates serological social contact data five European countries\" PLoS Medicine 7, . 6 (June 1, 2010):e1000291. doi:10.1371/journal.pmed.1000291. Simonsen, J., Strid, M. ., Molbak, K., Krogfelt, K. ., Linneberg, ., Teunis, P. \"Sero-epidemiology tool study incidence Salmonella infections humans\" Epidemiology Infection 136, . 7 (July 1, 2008): 895--902. doi:10.1017/S0950268807009314 Simonsen, J., Teunis, P. F., van Pelt, W., van Duynhoven, Y., Krogfelt, K. ., Sadkowska-Todys, M., Molbak K. \"Usefulness seroconversion rates comparing infection pressures countries\" Epidemiology Infection, April 12, 2010, 1--8. doi:10.1017/S0950268810000750. Falkenhorst, G., Simonsen, J., Ceper, T. H., van Pelt, W., de Valk, H., Sadkowska-Todys, M., Zota, L., Kuusi, M., Jernberg, C., Rota, M. C., van Duynhoven, Y. T., Teunis, P. F., Krogfelt, K. ., Molbak, K. \"Serological cross-sectional studies salmonella incidence eight European countries: correlation incidence reported cases\" BMC Public Health 12, . 1 (July 15, 2012): 523--23. doi:10.1186/1471-2458-12-523. Teunis, P. F., Falkenhorst, G., Ang, C. W., Strid, M. ., De Valk, H., Sadkowska-Todys, M., Zota, L., Kuusi, M., Rota, M. C., Simonsen, J. B., Molbak, K., Van Duynhoven, Y. T., van Pelt, W. \"Campylobacter seroconversion rates selected countries European Union\" Epidemiology Infection 141, . 10 (2013): 2051--57. doi:10.1017/S0950268812002774. de Melker, H. E., Versteegh, F. G., Schellekens, J. F., Teunis, P. F., Kretzschmar, M. \"incidence Bordetella pertussis infections estimated population combination serological surveys\" Journal Infection 53, . 2 (August 1, 2006): 106--13. doi:10.1016/j.jinf.2005.10.020 Quantification seroresponse de Graaf, W. F., Kretzschmar, M. E., Teunis, P. F., Diekmann, O. \"two-phase within-host model immune response application serological profiles pertussis\" Epidemics 9 (2014):1--7. doi:10.1016/j.epidem.2014.08.002. Berbers, G. ., van de Wetering, M. S., van Gageldonk, P. G., Schellekens, J. F., Versteegh, F. G., Teunis, P.F. \"novel method evaluating natural vaccine induced serological responses Bordetella pertussis antigens\" Vaccine 31, . 36 (August 12, 2013): 3732--38. doi:10.1016/j.vaccine.2013.05.073. Versteegh, F. G., Mertens, P. L., de Melker, H. E., Roord, J. J., Schellekens, J. F., Teunis, P. F. \"Age-specific long-term course IgG antibodies pertussis toxin symptomatic infection Bordetella pertussis\" Epidemiology Infection 133, . 4 (August 1, 2005): 737--48. Teunis, P. F., van der Heijden, O. G., de Melker, H. E., Schellekens, J. F., Versteegh, F. G., Kretzschmar, M. E. \"Kinetics IgG antibody response pertussis toxin infection B. pertussis\" Epidemiology Infection 129, . 3 (December 10, 2002):479. doi:10.1017/S0950268802007896.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/serocalculator.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimating Infection Rates from Serological Data — serocalculator","text":"Peter Teunis p.teunis@emory.edu Doug Ezra Morrison demorrison@ucdavis.edu Kristen Aiemjoy kaiemjoy@ucdavis.edu Kristina Lai kwlai@ucdavis.edu","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate a cross-sectional serosurvey with noise — sim.cs","title":"Simulate a cross-sectional serosurvey with noise — sim.cs","text":"Makes cross-sectional data set (age, y(t) set) adds noise, desired.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate a cross-sectional serosurvey with noise — sim.cs","text":"","code":"sim.cs(   lambda = 0.1,   n.smpl = 100,   age.rng = c(0, 20),   age.fx = NA,   antigen_isos,   n.mc = 0,   renew.params = FALSE,   add.noise = FALSE,   curve_params,   noise_limits,   format = \"wide\",   verbose = FALSE,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate a cross-sectional serosurvey with noise — sim.cs","text":"lambda numeric() scalar indicating incidence rate (events per person-years) n.smpl number samples simulate age.rng age range sampled individuals, years age.fx specify curve parameters use age (nothing present?) antigen_isos Character vector one antibody names. Values must match curve_params. n.mc many MCMC samples use: n.mc 1:4000 fixed posterior sample used n.mc = 0, random sample chosen renew.params whether generate new parameter set infection renew.params = TRUE generates new parameter set infection renew.params = FALSE keeps one selected birth, updates baseline y0 add.noise logical() indicating whether add biological measurement noise curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay noise_limits biologic noise distribution parameters format character() variable, containing either: \"long\" (one measurement per row) \"wide\" (one serum sample per row) verbose logical: TRUE, print verbose log information console ... Arguments passed simcs.tinf","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate a cross-sectional serosurvey with noise — sim.cs","text":"tibble::tbl_df containing simulated cross-sectional serosurvey data, columns: age: age (days) one column element antigen_iso input argument","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate a cross-sectional serosurvey with noise — sim.cs","text":"","code":"#Load curve parameters dmcmc = load_curve_params(\"https://osf.io/download/rtw5k\")  #Specify the antibody-isotype responses to include in analyses antibodies = c(\"HlyE_IgA\", \"HlyE_IgG\")  #set seed to reproduce results set.seed(54321)  # simulated incidence rate per person-year lambda <- 0.2;  # range covered in simulations lifespan <- c(0, 10);  # cross-sectional sample size nrep <- 100  # biologic noise distribution dlims <- rbind(   \"HlyE_IgA\" = c(min = 0, max = 0.5),   \"HlyE_IgG\" = c(min = 0, max = 0.5))  #generate cross-sectional data csdata <- sim.cs(   curve_params = dmcmc,   lambda = lambda,   n.smpl = nrep,   age.rng = lifespan,   antigen_isos = antibodies,  n.mc = 0,  renew.params = TRUE,  add.noise = TRUE,  noise_limits = dlims,  format = \"long\" )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.multi.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate multiple data sets — sim.cs.multi","title":"Simulate multiple data sets — sim.cs.multi","text":"Simulate multiple data sets","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.multi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate multiple data sets — sim.cs.multi","text":"","code":"sim.cs.multi(   nclus = 10,   lambdas = c(0.05, 0.1, 0.15, 0.2, 0.3),   num_cores = max(1, parallel::detectCores() - 1),   rng_seed = 1234,   renew.params = TRUE,   add.noise = TRUE,   verbose = FALSE,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.multi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate multiple data sets — sim.cs.multi","text":"nclus number clusters lambdas #incidence rate, events/person*year num_cores number cores use parallel computations rng_seed starting seed random number generator, passed rngtools::RNGseq() renew.params whether generate new parameter set infection renew.params = TRUE generates new parameter set infection renew.params = FALSE keeps one selected birth, updates baseline y0 add.noise logical() indicating whether add biological measurement noise verbose whether report verbose information ... Arguments passed sim.cs lambda numeric() scalar indicating incidence rate (events per person-years) n.smpl number samples simulate age.rng age range sampled individuals, years age.fx specify curve parameters use age (nothing present?) antigen_isos Character vector one antibody names. Values must match curve_params. n.mc many MCMC samples use: n.mc 1:4000 fixed posterior sample used n.mc = 0, random sample chosen noise_limits biologic noise distribution parameters format character() variable, containing either: \"long\" (one measurement per row) \"wide\" (one serum sample per row) curve_params data.frame() containing MCMC samples parameters Bayesian posterior distribution longitudinal decay curve model. parameter columns must named: antigen_iso: character() vector indicating antigen-isotype combinations iter: integer() vector indicating MCMC sampling iterations y0: baseline antibody level $t=0$ ($y(t=0)$) y1: antibody peak level (ELISA units) t1: duration infection alpha: antibody decay rate (1/days current longitudinal parameter sets) r: shape factor antibody decay","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sim.cs.multi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate multiple data sets — sim.cs.multi","text":"tibble::tibble()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/simcs.tinf.html","id":null,"dir":"Reference","previous_headings":"","what":"collect cross-sectional data — simcs.tinf","title":"collect cross-sectional data — simcs.tinf","text":"output: (age, y(t) set)","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/simcs.tinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"collect cross-sectional data — simcs.tinf","text":"","code":"simcs.tinf(   lambda,   n.smpl,   age.rng,   age.fx = NA,   antigen_isos,   n.mc = 0,   renew.params = FALSE,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/simcs.tinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"collect cross-sectional data — simcs.tinf","text":"lambda seroconversion rate (events/person-day) n.smpl number samples n.smpl (= nr simulated records) age.rng age range use simulating data, days age.fx age.fx parameter sample (age.fx = NA age infection) antigen_isos Character vector one antibody names. Values must match curve_params. n.mc n.mc 1:4000 fixed posterior sample used n.mc = 0 random sample chosen renew.params renew.params = TRUE generates new parameter set infection renew.params = FALSE keeps one selected birth, updates baseline y0 ... arguments passed simresp.tinf()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/simcs.tinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"collect cross-sectional data — simcs.tinf","text":"array()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/simresp.tinf.html","id":null,"dir":"Reference","previous_headings":"","what":"simulate antibody kinetics of y over a time interval — simresp.tinf","title":"simulate antibody kinetics of y over a time interval — simresp.tinf","text":"simulate antibody kinetics y time interval","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/simresp.tinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"simulate antibody kinetics of y over a time interval — simresp.tinf","text":"","code":"simresp.tinf(   lambda,   t.end,   age.fx,   antigen_isos,   n.mc = 0,   renew.params,   predpar,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/simresp.tinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"simulate antibody kinetics of y over a time interval — simresp.tinf","text":"lambda seroconversion rate (1/days), t.end end time interval (beginning time 0) days(?) age.fx parameter estimates fixed age (age.fx years) . age.fx = NA age infection used. antigen_isos antigen isotypes n.mc posterior sample may selected (1:4000), n.mc = 0 posterior sample chosen random. renew.params infection, new parameter sample may generated (renew.params = TRUE). Otherwise (renew.params = FALSE), sample generated birth kept, baseline y0 carried prior infections. predpar array() dimensions named: antigen_iso parameter obs ... Arguments passed ldpar, ab, mkbaseline age age infection nmc mcmc sample use npar number parameters t age infection? par parameters kab index row antibody baseline limits read blims n number observations blims range possible baseline antibody levels","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/simresp.tinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"simulate antibody kinetics of y over a time interval — simresp.tinf","text":"function returns list() : t = times (days, birth day 0), b = bacteria level, antibody signal (used; probably meaningless), y = antibody level, antibody signal smp = whether infection involves big jump small jump t.inf = times infections occurred.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract strata from an object — strata","title":"Extract strata from an object — strata","text":"Generic method extracting strata objects. See strata.seroincidence.()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract strata from an object — strata","text":"","code":"strata(x)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract strata from an object — strata","text":"x object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract strata from an object — strata","text":"strata x","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the Strata attribute from an object, if present — strata.seroincidence.by","title":"Extract the Strata attribute from an object, if present — strata.seroincidence.by","text":"Extract Strata attribute object, present","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the Strata attribute from an object, if present — strata.seroincidence.by","text":"","code":"# S3 method for seroincidence.by strata(x)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the Strata attribute from an object, if present — strata.seroincidence.by","text":"x R object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/strata.seroincidence.by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the Strata attribute from an object, if present — strata.seroincidence.by","text":"tibble::tibble() strata rows, NULL x \"strata\" attribute","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sub-.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or replace parts of a seroincidence.by object — [.seroincidence.by","title":"Extract or replace parts of a seroincidence.by object — [.seroincidence.by","text":"Extract replace parts seroincidence.object","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sub-.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract or replace parts of a seroincidence.by object — [.seroincidence.by","text":"","code":"# S3 method for seroincidence.by [(x, i, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/sub-.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract or replace parts of a seroincidence.by object — [.seroincidence.by","text":"x object subset/replace elements indices subset/replace ... passed [.list","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/sub-.seroincidence.by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or replace parts of a seroincidence.by object — [.seroincidence.by","text":"subset specified","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.pop_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize a cross-sectional antibody survey data set — summary.pop_data","title":"Summarize a cross-sectional antibody survey data set — summary.pop_data","text":"function summary() method pop_data objects","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.pop_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize a cross-sectional antibody survey data set — summary.pop_data","text":"","code":"# S3 method for pop_data summary(object, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.pop_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize a cross-sectional antibody survey data set — summary.pop_data","text":"object pop_data object ... unused","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.pop_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize a cross-sectional antibody survey data set — summary.pop_data","text":"list containing two summary tables: one age one value, stratified antigen_iso","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.pop_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize a cross-sectional antibody survey data set — summary.pop_data","text":"","code":"library(dplyr)  xs_data <- \"https://osf.io/download//n6cp3/\" %>% load_pop_data() %>% clean_pop_data()  xs_data %>% summary() #>  #> n = 3336  #>  #> Distribution of age:  #>  #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.  #>    0.60    5.00   10.00   10.53   15.00   25.00  #>  #> Distributions of antigen-isotype measurements: #>  #>   antigen_iso Min   1st Qu.   Median  3rd Qu.      Max # NAs #> 1    HlyE_IgA   0 0.8513305 1.736334 3.656655 133.3666     0 #> 2    HlyE_IgG   0 1.1485953 2.695958 6.737705 218.6229     0"},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.by.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Method for ","title":"Summary Method for ","text":"Calculate seroincidence output seroincidence calculator est.incidence.().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Method for ","text":"","code":"# S3 method for seroincidence.by summary(   object,   confidence_level = 0.95,   showDeviance = TRUE,   showConvergence = TRUE,   ... )"},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Method for ","text":"object dataframe containing output function est.incidence.(). confidence_level desired confidence interval coverage probability showDeviance Logical flag (FALSE/TRUE) reporting deviance (-2*log(likelihood) estimated seroincidence. Default = TRUE. showConvergence Logical flag (FALSE/TRUE) reporting convergence (see help optim() details). Default = FALSE. ... Additional arguments affecting summary produced.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Method for ","text":"summary.seroincidence.object, tibble::tibble, following columns: incidence.rate maximum likelihood estimate lambda (seroincidence) CI.lwr lower confidence bound lambda CI.upr upper confidence bound lambda Deviance (included showDeviance = TRUE) Negative log likelihood (NLL) estimated (maximum likelihood) lambda) nlm.convergence.code (included showConvergence = TRUE) Convergence information returned stats::nlm() object also following metadata (accessible base::attr()): antigen_isos Character vector names input antigen isotypes used est.incidence.() Strata Character names strata used est.incidence.()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Method for ","text":"","code":"library(dplyr)  xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\")%>%   clean_pop_data() curve = load_curve_params(\"https://osf.io/download/rtw5k/\" ) noise = load_noise_params(\"https://osf.io/download//hqy4v/\")  est2 = est.incidence.by( strata = c(\"catchment\"), pop_data = xs_data %>% filter(Country == \"Pakistan\"), curve_params = curve, noise_params = noise %>% filter(Country == \"Pakistan\"), antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\"), #num_cores = 8 #Allow for parallel processing to decrease run time ) #> data format is as expected. #> Warning: curve_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> Warning: noise_params is missing all strata variables, and will be used unstratified. #>  #> To avoid this warning, specify the desired set of stratifying variables in the `curve_strata_varnames` and `noise_strata_varnames` arguments to `est.incidence.by()`. #> data format is as expected. #> data format is as expected.  summary(est2) #> Seroincidence estimated given the following setup: #> a) Antigen isotypes   : HlyE_IgG, HlyE_IgA  #> b) Strata       : catchment  #>  #>  Seroincidence estimates: #> # A tibble: 2 × 13 #>   Stratum   catchment     n est.start incidence.rate      SE CI.lwr CI.upr #>   <chr>     <chr>     <int>     <dbl>          <dbl>   <dbl>  <dbl>  <dbl> #> 1 Stratum 1 aku         294       0.1          0.106 0.00767 0.0916  0.122 #> 2 Stratum 2 kgh         200       0.1          0.167 0.0133  0.143   0.196 #> # ℹ 5 more variables: coverage <dbl>, log.lik <dbl>, iterations <int>, #> #   antigen.isos <chr>, nlm.convergence.code <ord>  if (FALSE) { # estimate seroincidence seroincidence <- est.incidence.by(...)  # calculate summary statistics for the seroincidence object seroincidenceSummary <- summary(seroincidence) }"},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarizing fitted seroincidence models — summary.seroincidence","title":"Summarizing fitted seroincidence models — summary.seroincidence","text":"function summary() method seroincidence objects.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarizing fitted seroincidence models — summary.seroincidence","text":"","code":"# S3 method for seroincidence summary(object, coverage = 0.95, ...)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarizing fitted seroincidence models — summary.seroincidence","text":"object list(), outputted stats::nlm() est.incidence() coverage desired confidence interval coverage probability ... unused","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarizing fitted seroincidence models — summary.seroincidence","text":"tibble::tibble() containing following: est.start: starting guess incidence rate ageCat: age category analyzing incidence.rate: estimated incidence rate, per person year CI.lwr: lower limit confidence interval incidence rate CI.upr: upper limit confidence interval incidence rate coverage: coverage probability log.lik: log-likelihood data used call est.incidence(), evaluated maximum-likelihood estimate lambda (.e., incidence.rate) iterations: number iterations used antigen_isos: list antigen isotypes used analysis nlm.convergence.code: information convergence likelihood maximization procedure performed nlm() (see \"Value\" section stats::nlm(), component code); codes 3-5 indicate issues: 1: relative gradient close zero, current iterate probably solution. 2: successive iterates within tolerance, current iterate probably solution. 3: Last global step failed locate point lower x. Either x approximate local minimum function, function non-linear algorithm, stepmin est.incidence() (.k.., steptol stats::nlm()) large. 4: iteration limit exceeded; increase iterlim. 5: maximum step size stepmax exceeded five consecutive times. Either function unbounded , becomes asymptotic finite value direction stepmax small.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/summary.seroincidence.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarizing fitted seroincidence models — summary.seroincidence","text":"","code":"library(dplyr)  xs_data = load_pop_data(\"https://osf.io/download//n6cp3/\") %>%   clean_pop_data() curve = load_curve_params(\"https://osf.io/download/rtw5k/\") noise = load_noise_params(\"https://osf.io/download//hqy4v/\")  est1 = est.incidence( pop_data = xs_data %>% filter(Country == \"Pakistan\"), curve_param = curve, noise_param = noise %>% filter(Country == \"Pakistan\"), antigen_isos = c(\"HlyE_IgG\", \"HlyE_IgA\") ) #> data format is as expected.  summary(est1) #> # A tibble: 1 × 10 #>   est.start incidence.rate      SE CI.lwr CI.upr coverage log.lik iterations #>       <dbl>          <dbl>   <dbl>  <dbl>  <dbl>    <dbl>   <dbl>      <int> #> 1       0.1          0.128 0.00682  0.115  0.142     0.95  -2376.          4 #> # ℹ 2 more variables: antigen.isos <chr>, nlm.convergence.code <ord>"},{"path":"https://ucd-serg.github.io/serocalculator/reference/warn.missing.strata.html","id":null,"dir":"Reference","previous_headings":"","what":"Warn about missing stratifying variables in a dataset — warn.missing.strata","title":"Warn about missing stratifying variables in a dataset — warn.missing.strata","text":"Warn missing stratifying variables dataset","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/warn.missing.strata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Warn about missing stratifying variables in a dataset — warn.missing.strata","text":"","code":"warn.missing.strata(data, strata, dataname)"},{"path":"https://ucd-serg.github.io/serocalculator/reference/warn.missing.strata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Warn about missing stratifying variables in a dataset — warn.missing.strata","text":"data dataset contain strata strata data.frame() showing strata levels expected dataset dataname name dataset, use warning messages strata missing.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/warn.missing.strata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Warn about missing stratifying variables in a dataset — warn.missing.strata","text":"character() vector subset stratifying variables present pop_data","code":""},{"path":"https://ucd-serg.github.io/serocalculator/reference/warn.missing.strata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Warn about missing stratifying variables in a dataset — warn.missing.strata","text":"","code":"if (FALSE) { expected_strata = data.frame(Species = \"banana\", type = \"orchid\") warn.missing.strata(iris, expected_strata, dataname = \"iris\") }"},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-100","dir":"Changelog","previous_headings":"","what":"serocalculator 1.0.0","title":"serocalculator 1.0.0","text":"Moved underlying methods serocalculator vignette","code":""},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-050","dir":"Changelog","previous_headings":"","what":"serocalculator 0.5.0","title":"serocalculator 0.5.0","text":"Spell-checking function documentation tutorial articles. Added functions methods: load_pop_data() check_pop_data() clean_pop_data() summary.pop_data() autoplot.pop_data() load_curve_params() Renamed graph.decay.curves.() autoplot.curve_params()","code":""},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-040","dir":"Changelog","previous_headings":"","what":"serocalculator 0.4.0","title":"serocalculator 0.4.0","text":"plot() methods renamed autoplot(), matching general convention ggplot2-based graphics. added visualization curve parameters sim.cs() now format argument specify long wide format output.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-0-4-0","dir":"Changelog","previous_headings":"","what":"serocalculator 0.3.2","title":"serocalculator 0.4.0","text":"Fixed bug passing antigen_isos est.incidence.() est.incidence().","code":""},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-0-4-0-1","dir":"Changelog","previous_headings":"","what":"serocalculator 0.3.1","title":"serocalculator 0.4.0","text":"Rolled back required R version 4.2 4.1","code":""},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-030","dir":"Changelog","previous_headings":"","what":"serocalculator 0.3.0","title":"serocalculator 0.3.0","text":"Fixed stability documentation-clarity issues initial tester feedback.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-020-never-formally-incremented-in-description","dir":"Changelog","previous_headings":"","what":"serocalculator 0.2.0 (never formally incremented in DESCRIPTION)","title":"serocalculator 0.2.0 (never formally incremented in DESCRIPTION)","text":"Added new vignettes.","code":""},{"path":"https://ucd-serg.github.io/serocalculator/news/index.html","id":"serocalculator-010","dir":"Changelog","previous_headings":"","what":"serocalculator 0.1.0","title":"serocalculator 0.1.0","text":"Forking seroincidence package adding Teunis et al 2020 approach.","code":""}]