use dfault method for priors

NAMESPACE

@@ -1,11 +1,11 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method(plot,biokinetics_population_trajectories)
+S3method(plot,biokinetics_priors)
 export(add_exposure_data)
 export(biokinetics)
 export(biokinetics_priors)
 export(convert_log2_scale_inverse)
-export(plot_prior_predictive)
 importFrom(R6,R6Class)
 importFrom(data.table,":=")
 importFrom(data.table,.BY)

R/biokinetics.R

@@ -278,10 +278,10 @@ biokinetics <- R6::R6Class(
     #' @param n_draws Integer. The number of trajectories to simulate. Default 2000.
     plot_prior_predictive = function(tmax = 150,
                                      n_draws = 2000) {
-      plot_prior_predictive(private$priors,
-                            tmax = tmax,
-                            n_draws = n_draws,
-                            data = private$data)
+      plot(private$priors,
+           tmax = tmax,
+           n_draws = n_draws,
+           data = private$data)
     },
     #' @description Plot model input data with a smoothing function. Note that
     #' this plot is on a log scale, regardless of whether data was provided on a

R/plot.R

@@ -4,15 +4,16 @@
 #' @description Simulate trajectories by drawing random samples from the given
 #' priors for each parameter in the biokinetics model.
 #' @return A ggplot2 object.
-#' @param priors A named list of type 'biokinetics_priors'.
+#' @param x A named list of type 'biokinetics_priors'.
+#' @param \dots Further arguments passed to the method.
 #' @param tmax Integer. The number of time points in each simulated trajectory. Default 150.
 #' @param n_draws Integer. The number of trajectories to simulate. Default 2000.
 #' @param data Optional data.frame with columns time_since_last_exp and value. The raw data to compare to.
-plot_prior_predictive <- function(priors,
-                                  tmax = 150,
-                                  n_draws = 2000,
-                                  data = NULL) {
-  validate_priors(priors)
+plot.biokinetics_priors <- function(x,
+                                    ...,
+                                    tmax = 150,
+                                    n_draws = 2000,
+                                    data = NULL) {
 
   # Declare variables to suppress notes when compiling package
   # https://github.com/Rdatatable/data.table/issues/850#issuecomment-259466153
@@ -23,12 +24,12 @@ plot_prior_predictive <- function(priors,
     validate_required_cols(data, c("time_since_last_exp", "value"))
   }
   params <- data.table(
-    t0 = stats::rnorm(n_draws, priors$mu_t0, priors$sigma_t0), # titre value at t0
-    tp = stats::rnorm(n_draws, priors$mu_tp, priors$sigma_tp), # time of peak
-    ts = stats::rnorm(n_draws, priors$mu_ts, priors$sigma_ts), # time of set point
-    m1 = stats::rnorm(n_draws, priors$mu_m1, priors$sigma_m1), # gradient 1
-    m2 = stats::rnorm(n_draws, priors$mu_m2, priors$sigma_m2), # gradient 2
-    m3 = stats::rnorm(n_draws, priors$mu_m3, priors$sigma_m3) # gradient 3
+    t0 = stats::rnorm(n_draws, x$mu_t0, x$sigma_t0), # titre value at t0
+    tp = stats::rnorm(n_draws, x$mu_tp, x$sigma_tp), # time of peak
+    ts = stats::rnorm(n_draws, x$mu_ts, x$sigma_ts), # time of set point
+    m1 = stats::rnorm(n_draws, x$mu_m1, x$sigma_m1), # gradient 1
+    m2 = stats::rnorm(n_draws, x$mu_m2, x$sigma_m2), # gradient 2
+    m3 = stats::rnorm(n_draws, x$mu_m3, x$sigma_m3) # gradient 3
   )
 
   times <- data.table(t = 1:tmax)

tests/testthat/test-plots.R

@@ -1,16 +1,16 @@
 test_that("Can plot prior prediction up to tmax", {
   priors <- biokinetics_priors()
-  plot <- plot_prior_predictive(priors, tmax = 100, n_draws = 500)
+  plot <- plot(priors, tmax = 100, n_draws = 500)
   expect_equal(nrow(plot$data), 100)
   expect_equal(length(plot$layers), 2)
 })
 
 test_that("Can plot prior prediction with data points", {
   data <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
   priors <- biokinetics_priors()
-  expect_error(plot_prior_predictive(priors, data = data), "Missing required columns: time_since_last_exp")
+  expect_error(plot(priors, data = data), "Missing required columns: time_since_last_exp")
   data[, `:=`(time_since_last_exp = as.integer(day - last_exp_day, units = "days"))]
-  plot <- plot_prior_predictive(priors, data = data, n_draws = 500)
+  plot <- plot(priors, data = data, n_draws = 500)
   expect_equal(length(plot$layers), 3)
 })