Merge pull request #189 from UCD-SERG/complete-rename

updating instances of deprecated function names
UCD-SERG · Jul 16, 2024 · be49297 · be49297
2 parents 662c265 + 98634ce
commit be49297
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Showing 5 changed files with 13 additions and 8 deletions.
diff --git a/R/llik.R b/R/llik.R
@@ -4,7 +4,7 @@
 #' Calculates the log-likelihood of a set of cross-sectional antibody response data, for a given incidence rate (`lambda`) value.
 #' `r lifecycle::badge("deprecated")`
 #'
-#' `llik()` was renamed to `log_likelihood()` to create a more
+#' `llik()` was renamed to [log_likelihood()] to create a more
 #' consistent API.
 #'
 #' @keywords internal
@@ -19,7 +19,12 @@ llik <- function(
     verbose = FALSE,
     ...) {
   lifecycle::deprecate_warn("1.0.0", "llik()", "log_likelihood()")
-  log_likelihood(lambda, pop_data, antigen_isos, curve_params, noise_params, verbose = FALSE)
+  log_likelihood(lambda,
+                 pop_data,
+                 antigen_isos,
+                 curve_params,
+                 noise_params,
+                 verbose)
 }
 
 #' Calculate log-likelihood

diff --git a/R/sim.cs-utils.r b/R/sim.cs-utils.r
@@ -85,7 +85,7 @@ baseline <- function(kab, yvec, blims, ...) {
   subst <- which(yvec < blims[kab, 2])
   k <- 1
   while (k <= length(subst)) {
-    yvec[subst[k]] <- mkbaseline(kab, 1, blims)
+    yvec[subst[k]] <- mk_baseline(kab, 1, blims)
     k <- k + 1
   }
   return(yvec)
@@ -94,7 +94,7 @@ baseline <- function(kab, yvec, blims, ...) {
 #' @title generate random sample from baseline distribution
 #' @description
 #' `r lifecycle::badge("deprecated")`
-#' `mkbaseline()` was renamed to `mk_baseline()` to create a more
+#' `mkbaseline()` was renamed to [mk_baseline()] to create a more
 #' consistent API.
 #' @param kab index for which row of antibody baseline limits to read from `blims`
 #' @param n number of observations

diff --git a/man/llik.Rd b/man/llik.Rd
diff --git a/man/mkbaseline.Rd b/man/mkbaseline.Rd
diff --git a/vignettes/articles/simulate_xsectionalData.Rmd b/vignettes/articles/simulate_xsectionalData.Rmd
@@ -167,15 +167,15 @@ ggplot(csdata, aes(
 We can calculate the log-likelihood of the data as a function of the incidence rate directly:
 
 ```{r}
-ll_A <- llik(
+ll_A <- log_likelihood(
   pop_data = csdata,
   curve_params = dmcmc,
   noise_params = cond,
   antigen_isos = "HlyE_IgA",
   lambda = 0.1
 ) %>% print()
 
-ll_G <- llik(
+ll_G <- log_likelihood(
   pop_data = csdata,
   curve_params = dmcmc,
   noise_params = cond,