more fixes to website

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: serocalculator
 Title: Estimating Infection Rates from Serological Data
-Version: 1.2.0.9015
+Version: 1.2.0.9016
 Authors@R: c(
     person("Peter", "Teunis", , "[email protected]", role = c("aut", "cph"),
            comment = "Author of the method and original code."),

NEWS.md

@@ -4,13 +4,14 @@
 
 * Add `as_noise_params` (#228) 
 
-* Updated `simulate_xsectionalData.Rmd()` (linting, removing deprecated functions).
+* Updated `simulate_xsectionalData.Rmd` (linting, removing deprecated functions)
+(#289)
 
 * Added default value for `antigen_isos` argument in `log_likelihood()` (#286)
 
 * Updated enteric fever example article with upgraded code and visualizations (#290)
 
-* Added `Methodology` vignette (#284, #302)
+* Added `Methodology` vignette (#284, #302, #303)
 
 * Added template for reporting Issues 
 (from `usethis::use_tidy_issue_template()`) (#270)

README.md

@@ -11,9 +11,14 @@
 [![codecov](https://codecov.io/gh/UCD-SERG/serocalculator/graph/badge.svg?token=85CXV6GN2T)](https://codecov.io/gh/UCD-SERG/serocalculator)
 <!-- badges: end -->
 
-    #> Warning: package 'qrcode' was built under R version 4.4.1
+<div id="fig-website-QR">
 
-![QR code for {serocalculator} website](man/figures/qr.svg)
+![](man/figures/qr.svg)
+
+
+Figure 1: QR code for `serocalculator` website
+
+</div>
 
 Antibody levels measured in a cross–sectional population sample can be
 translated into an estimate of the frequency with which seroconversions

README.qmd

@@ -29,7 +29,7 @@ code <- qr_code("https://ucd-serg.github.io/serocalculator/")
 generate_svg(code, filename = "man/figures/qr.svg")
 ```
 
-![QR code for {serocalculator} website](man/figures/qr.svg)
+![QR code for `serocalculator` website](man/figures/qr.svg){#fig-website-QR}
 
 Antibody levels measured in a cross–sectional population sample can be translated into an estimate of the frequency with which seroconversions (infections) occur in the sampled population. 
 In other words, the presence of many high antibody titers indicates that many individuals likely experienced infection recently and the burden of disease is high in the population, 

_pkgdown.yml

@@ -3,3 +3,4 @@ template:
   bootstrap: 5
   light-switch: true
 
+bibliography: vignettes/references.bib

vignettes/.gitignore

@@ -7,5 +7,5 @@ methodology_files
 *_files/
 *.pptx
 *.docx
-
 /.quarto/
+*.rmarkdown

vignettes/articles/enteric_fever_example.Rmd

@@ -9,7 +9,7 @@ vignette: >
   %\VignetteIndexEntry{Enteric Fever Seroincidence Vignette}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
-
+bibliography: ../references.bib
 ---
 ## Introduction
 

vignettes/articles/scrubTyphus_example.Rmd

@@ -9,7 +9,7 @@ vignette: >
   %\VignetteIndexEntry{Scrub Typhus Seroincidence Vignette}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
-
+bibliography: ../references.bib
 
 ---
 ## Introduction
@@ -42,17 +42,23 @@ knitr::opts_chunk$set(
 
 
 ### Load packages
-The first step in conducting this analysis is to load our necessary packages. If you haven't installed already, you will need to do so before loading. 
 
-```{r setup, message=FALSE}
-# devtools::install_github("ucd-serg/serocalculator", eval=FALSE)
-```
+The first step in conducting this analysis is to load our necessary packages. 
+If you haven't installed already, you will need to do so before loading. 
+We will also need to have the `tidyverse` and `mixtools` packages installed
+for data manipulation and graphics operations we will perform in this vignette.
+Please see the websites for 
+[`serocalculator`](https://ucd-serg.github.io/serocalculator/#installing-the-serocalculator-package),
+[`tidyverse`](https://tidyverse.tidyverse.org/#installation),
+and [`mixtools`](https://github.com/dsy109/mixtools?tab=readme-ov-file#installation)
+for guidance on installing these packages into your R package library.
+
+Once all three of those packages are installed, we can load them into 
+our active R session environment:
 
 ```{r load_packages}
 library(serocalculator)
-# install.packages("tidyverse")
 library(tidyverse)
-# install.packages("mixtools")
 library(mixtools)
 ```
 
@@ -147,29 +153,31 @@ Column Name | Description
 
 ```{r message=FALSE, warning=FALSE}
 # biologic noise
-b.noise <- xs_data %>%
+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(.$value, k = 2, maxit = 1000) # k is the number of components, adjust as necessary
-    # Assuming the first component is the lower distribution
+    mixmod <- normalmixEM(.$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 = .$antigen_iso[1], percentile75 = percentile75)
+    data.frame(antigen_iso = .$antigen_iso[1],
+               percentile75 = percentile75)
   })
 
 
 
 # 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)
+  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)
@@ -236,25 +244,22 @@ estdf <- summary(est) %>%
 est2df <- summary(est2)
 
 
-est.comb <- rbind(estdf, est2df)
+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")) +
+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"
-  ) +
+                 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)
-  ) +
+  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", "red"))
 ```
 

vignettes/articles/serocalculator.Rmd

@@ -1,12 +1,13 @@
 ---
-title: "Overview"
+title: "Introduction to serocalculator"
 description: >
   A summary of the methods behind serocalculator.
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Overview}
+  %\VignetteIndexEntry{Introduction to serocalculator}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
+bibliography: ../references.bib
 ---
 
 ## Overview

vignettes/articles/simulate_xsectionalData.Rmd

@@ -11,7 +11,8 @@
   %\VignetteIndexEntry{simulate_xsectionalData}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
-  ---
+bibliography: ../references.bib
+---
 
 
 This vignette shows how to simulate a cross-sectional sample of 
@@ -219,7 +220,7 @@
 We can also graph the log-likelihoods, even without finding the MLEs, using `graph_loglik()`:
 
 ```{r}
 lik_HlyE_IgA <-
   graph_loglik(
     pop_data = csdata,
     curve_params = dmcmc,
@@ -228,7 +229,7 @@
     log_x = TRUE
   )
 
 lik_HlyE_IgG <- graph_loglik(
   previous_plot = lik_HlyE_IgA,
   pop_data = csdata,
   curve_params = dmcmc,

vignettes/methodology.qmd

@@ -1,7 +1,7 @@
 ---
-title: "Introduction to seroincidence estimation"
+title: "Methodology"
 vignette: >
-  %\VignetteIndexEntry{Introduction to seroincidence estimation}
+  %\VignetteIndexEntry{Methodology}
   %\VignetteEngine{quarto::html}
   %\VignetteEncoding{UTF-8}
 ---
@@ -204,7 +204,7 @@
 ```{r}
 #| fig-cap: "Example log(likelihood) curves"
 #| label: fig-loglik
 lik_HlyE_IgA <- graph_loglik(
   pop_data = xs_data,
   curve_params = curves,
   noise_params = noise,
@@ -212,7 +212,7 @@
   log_x = TRUE
 )
 
 lik_HlyE_IgG <- graph_loglik(
   previous_plot = lik_HlyE_IgA,
   pop_data = xs_data,
   curve_params = curves,