Merge branch 'main' into auto-document

R/add_point_to_graph.R

@@ -5,7 +5,7 @@ add_point_to_graph <- function(
     point_data =
       tibble(
         x = fit$estimate %>% exp(),
-        y = llik(.data$x, ...),
+        y = log_likelihood(.data$x, ...),
         label = "est.incidence"
       ),
     ...) {

R/build_likelihood_function.R

@@ -10,7 +10,7 @@ build_likelihood_function <- function(
     for (cur_antigen in antigen_isos)
     {
       res <- res +
-        fdev(
+        f_dev(
           lambda = lambda,
           csdata = cross_sectional_data[[cur_antigen]],
           lnpars = longitudinal_parameter_samples[[cur_antigen]],

R/graph.loglik.R

@@ -68,7 +68,7 @@ graph.loglik = function(
   plot_data <-
     tibble(
       x = x %>% sort(),
-      y = llik(
+      y = log_likelihood(
         pop_data = pop_data,
         curve_params = curve_params,
         noise_params = noise_params,
@@ -94,7 +94,7 @@ graph.loglik = function(
         ggplot2::geom_point(
           data = tibble(
             x = highlight_points,
-            y = llik(
+            y = log_likelihood(
               pop_data = pop_data,
               curve_params = curve_params,
               noise_params = noise_params,

R/llik.R

@@ -68,7 +68,7 @@ llik <- function(
 #' ) # high cutoff (y.high)
 #'
 #' # Calculate log-likelihood
-#' ll_AG <- llik(
+#' ll_AG <- log_likelihood(
 #'   pop_data = xs_data,
 #'   curve_params = dmcmc,
 #'   noise_params = cond,
@@ -120,14 +120,14 @@ log_likelihood <- function(
     }
 
     nllSingle <-
-      fdev(
+      f_dev(
         lambda = lambda,
         csdata = cur_data,
         lnpars = cur_curve_params,
         cond = cur_noise_params
       )
 
-    # if (!is.na(nllSingle))  # not meaningful for vectorized fdev()
+    # if (!is.na(nllSingle))  # not meaningful for vectorized f_dev()
     {
       nllTotal <- nllTotal + nllSingle # DEM note: summing log likelihoods represents an independence assumption for multiple Antibodies, given time since seroconversion
     }

R/nll.R

@@ -1,11 +1,11 @@
 #' Calculate negative log-likelihood
 #' @description
-#' Same as [llik()], except negated and requiring lambda on log scale (used in combination with `nlm()`, to ensure that the optimization search doesn't stray into negative values of `lambda`).
+#' Same as [log_likelihood()], except negated and requiring lambda on log scale (used in combination with `nlm()`, to ensure that the optimization search doesn't stray into negative values of `lambda`).
 #' @param log.lambda natural logarithm of incidence rate
-#' @inheritDotParams llik -lambda
+#' @inheritDotParams log_likelihood -lambda
 
 #' @return the negative log-likelihood of the data with the current parameter values
 #' @keywords internal
 .nll <- function(log.lambda, ...) {
-  -llik(lambda = exp(log.lambda), ...)
+  -log_likelihood(lambda = exp(log.lambda), ...)
 }

R/sim.cs.R

@@ -94,7 +94,7 @@ sim.cs <- function(
     filter(.data$antigen_iso %in% antigen_isos) %>%
     droplevels() %>%
     prep_curve_params_for_array() %>%
-    df.to.array(dim_var_names = c("antigen_iso", "parameter"))
+    df_to_array(dim_var_names = c("antigen_iso", "parameter"))
 
   stopifnot(length(lambda) == 1)
 

data-raw/typhoid_results.qmd

@@ -70,7 +70,7 @@ log.lmax <- log(10 * lambda)
 
 
 objfunc <- function(llam) {
-  return(res <- fdev(llam, p.hlye.IgG, c.hlye.IgG, cond.hlye.IgG))
+  return(res <- f_dev(llam, p.hlye.IgG, c.hlye.IgG, cond.hlye.IgG))
 }