- introduced S3 methods for modelFits and postList: plot and predict …

…for the former, print and summary for the latter

NAMESPACE

@@ -1,8 +1,11 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(plot,modelFits)
+S3method(predict,ModelFits)
+S3method(print,postList)
+S3method(summary,postList)
 export(BMCPMod)
 export(getBootstrapBands)
 export(getModelFits)
 export(getPosterior)
-export(plot_modelFits)
 export(simulateData)

R/modelling.R

@@ -168,14 +168,16 @@ getModelFitOpt <- function (
 
 }
 
+#' @export
 predict.ModelFits <- function (
 
-  model_fits,
-  doses = NULL
+  object,
+  doses = NULL,
+  ...
 
 ) {
 
-  lapply(model_fits, predictModelFit, doses = doses)
+  lapply(x, predictModelFit, doses = doses, ...)
 
 }
 

R/plot.R

@@ -1,6 +1,6 @@
-#' @title plot_modelFits
+#' @title plot.modelFits
 #'
-#' @param model_fits tbd
+#' @param x tbd An object of type modelFits
 #' @param gAIC tbd
 #' @param avg_fit tbd
 #' @param cr_intv tbd
@@ -9,29 +9,32 @@
 #' @param alpha_CrB tbd
 #' @param n_bs_smpl tbd
 #' @param acc_color tbd
+#' @param ... tbd
 #' 
 #' @return tbd
 #' @export
-plot_modelFits <- function (
+plot.modelFits <- function (
 
-  model_fits,
+  x,
   gAIC      = TRUE,
   avg_fit   = TRUE,
   cr_intv   = TRUE,
   alpha_CrI = 0.05,
   cr_bands  = FALSE,
   alpha_CrB = c(0.05, 0.5),
   n_bs_smpl = 1e3,
-  acc_color = "orange"
+  acc_color = "orange",
+  ...
 
 ) {
 
-  plot_res <- 1e2
+  plot_res   <- 1e2
+  model_fits <- x
 
   dose_levels  <- model_fits[[1]]$dose_levels
   post_summary <- summary.postList(
-    post_list = attr(model_fits, "posterior"),
-    probs     = c(alpha_CrI / 2, 0.5, 1 - alpha_CrI / 2))
+    object = attr(model_fits, "posterior"),
+    probs  = c(alpha_CrI / 2, 0.5, 1 - alpha_CrI / 2))
   doses        <- seq(from       = min(dose_levels),
                       to         = max(dose_levels),
                       length.out = plot_res)

R/posterior.R

@@ -84,27 +84,32 @@ getPostCombsI <- function (
 
 }
 
+#' @export
 summary.postList <- function (
 
-  post_list,
+  object,
   ...
 
 ) {
 
-  summary_list        <- lapply(post_list, summary, ...)
-  names(summary_list) <- names(post_list)
+  summary_list        <- lapply(object, summary, ...)
+  names(summary_list) <- names(object)
   summary_tab         <- do.call(rbind, summary_list)
 
   return (summary_tab)
 
 }
 
+#' @export
 print.postList <- function (
 
-  post_list
+  x,
+  ...
 
 ) {
 
+  post_list <- x
+
   getMaxDiff <- function (
 
     medians
@@ -133,6 +138,6 @@ print.postList <- function (
                        "Maximum Difference to Control and Dose Group",
                        "Posterior Distributions")
 
-  print(list_out)
+  print(list_out, ...)
 
 }

tests/testthat/test-plot.R

@@ -1,4 +1,4 @@
-test_that("Test plot_modelFits with different model_fits input", {
+test_that("Test plot.modelFits with different model_fits input", {
   library(BayesianMCPMod)
   library(clinDR)
   library(dplyr)
@@ -150,22 +150,22 @@ test_that("Test plot_modelFits with different model_fits input", {
   )
 
   # Test with default parameters and more models
-  plot1 <- plot_modelFits(fit)
+  plot1 <- plot.modelFits(fit)
   expect_s3_class(plot1, "ggplot")
 
   # Test with cr_intv = TRUE and more models
-  plot2 <- plot_modelFits(fit, cr_intv = TRUE)
+  plot2 <- plot.modelFits(fit, cr_intv = TRUE)
   expect_s3_class(plot2, "ggplot")
 
   # Test with gAIC = FALSE and more models
-  plot3 <- plot_modelFits(fit, gAIC = FALSE)
+  plot3 <- plot.modelFits(fit, gAIC = FALSE)
   expect_s3_class(plot3, "ggplot")
 
   # Test with avg_fit = FALSE and more models
-  plot4 <- plot_modelFits(fit, avg_fit = FALSE)
+  plot4 <- plot.modelFits(fit, avg_fit = FALSE)
   expect_s3_class(plot4, "ggplot")
 
   # Test with all non-default parameters and more models
-  plot5 <- plot_modelFits(fit, cr_intv = TRUE, gAIC = FALSE, avg_fit = FALSE)
+  plot5 <- plot.modelFits(fit, cr_intv = TRUE, gAIC = FALSE, avg_fit = FALSE)
   expect_s3_class(plot5, "ggplot")
 })

vignettes/analysis_normal.Rmd

@@ -171,7 +171,7 @@ fit<- getModelFits(
 ```
 
 ```{r}
-plot_modelFits(fit)
-plot_modelFits(fit_simple, cr_bands = TRUE)
+plot(fit)
+plot(fit_simple, cr_bands = TRUE)
 ```
 For the plotting the credible intervals are shown as well.