rand_forest() - aorsf

`time` -> `eval_time`

R/rand_forest-aorsf.R

@@ -1,30 +1,40 @@
 #' Internal helper function for aorsf objects
 #' @param object A model object from `aorsf::orsf()`.
 #' @param new_data A data frame to be predicted.
-#' @param time A vector of times to predict the survival probability.
+#' @param eval_time A vector of times to predict the survival probability.
+#' @param time Deprecated. A vector of times to predict the survival probability.
 #' @return A tibble with a list column of nested tibbles.
 #' @export
 #' @keywords internal
 #' @name aorsf_internal
 #' @examples
 #' library(aorsf)
 #' aorsf <- orsf(na.omit(lung), Surv(time, status) ~ age + ph.ecog, n_tree = 10)
-#' preds <- survival_prob_orsf(aorsf, lung[1:3, ], time = c(250, 100))
-survival_prob_orsf <- function(object, new_data, time) {
+#' preds <- survival_prob_orsf(aorsf, lung[1:3, ], eval_time = c(250, 100))
+survival_prob_orsf <- function(object, new_data, eval_time, time = deprecated()) {
+  if (lifecycle::is_present(time)) {
+    lifecycle::deprecate_warn(
+      "0.1.1.9002",
+      "survival_prob_orsf(time)",
+      "survival_prob_orsf(eval_time)"
+    )
+    eval_time <- time
+  }
+
   # This is not just a `post` hook in `set_pred()` because parsnip adds the
-  # argument `time` to the prediction call and `aorsf::predict.orsf_fit()`
-  # expects empty dots, i.e. no `time` argument.
+  # argument `eval_time` to the prediction call and `aorsf::predict.orsf_fit()`
+  # expects empty dots, i.e. no `eval_time` argument.
 
   res <- predict(
     object,
     new_data = new_data,
-    pred_horizon = time,
+    pred_horizon = eval_time,
     pred_type = "surv",
     na_action = "pass",
     boundary_checks = FALSE
   )
 
-  res <- matrix_to_nested_tibbles_survival(res, time)
+  res <- matrix_to_nested_tibbles_survival(res, eval_time)
 
   # return a tibble
   tibble(.pred = res)

R/rand_forest-data.R

@@ -197,7 +197,8 @@ make_rand_forest_aorsf <- function() {
       func = c(pkg = "censored", fun = "survival_prob_orsf"),
       args = list(
         object = rlang::expr(object$fit),
-        new_data = rlang::expr(new_data)
+        new_data = rlang::expr(new_data),
+        eval_time = rlang::expr(eval_time)
       )
     )
   )

tests/testthat/test-rand_forest-aorsf.R

@@ -52,10 +52,10 @@ test_that("survival predictions", {
 
   expect_error(
     predict(f_fit, lung_orsf, type = "survival"),
-    "When using 'type' values of 'survival' or 'hazard' are given"
+    "When using `type` values of 'survival' or 'hazard', a numeric vector"
   )
 
-  f_pred <- predict(f_fit, lung, type = "survival", time = c(100, 500, 1200))
+  f_pred <- predict(f_fit, lung, type = "survival", eval_time = c(100, 500, 1200))
 
   expect_s3_class(f_pred, "tbl_df")
   expect_equal(names(f_pred), ".pred")
@@ -66,7 +66,7 @@ test_that("survival predictions", {
   )
 
   cf_names <-
-    c(".time", ".pred_survival")
+    c(".eval_time", ".pred_survival")
 
   expect_true(
     all(purrr::map_lgl(
@@ -77,7 +77,7 @@ test_that("survival predictions", {
 
   # correct prediction times in object
   expect_equal(
-    tidyr::unnest(f_pred, cols = c(.pred))$.time,
+    tidyr::unnest(f_pred, cols = c(.pred))$.eval_time,
     rep(c(100, 500, 1200), nrow(lung))
   )
 
@@ -103,7 +103,7 @@ test_that("survival predictions", {
   )
 
   # equal predictions with multiple times and one testing observation
-  f_pred <- predict(f_fit, lung[1, ], type = "survival", time = c(100, 500, 1200))
+  f_pred <- predict(f_fit, lung[1, ], type = "survival", eval_time = c(100, 500, 1200))
 
   new_km <- predict(
     exp_f_fit,
@@ -124,7 +124,7 @@ test_that("survival predictions", {
   )
 
   # equal predictions with one time and multiple testing observation
-  f_pred <- predict(f_fit, lung, type = "survival", time = 306)
+  f_pred <- predict(f_fit, lung, type = "survival", eval_time = 306)
 
   new_km <- predict(
     exp_f_fit,
@@ -144,7 +144,7 @@ test_that("survival predictions", {
   )
 
   # equal predictions with one time and one testing observation
-  f_pred <- predict(f_fit, lung[1, ], type = "survival", time = 306)
+  f_pred <- predict(f_fit, lung[1, ], type = "survival", eval_time = 306)
 
   new_km <- predict(
     exp_f_fit,
@@ -195,13 +195,13 @@ test_that("`fix_xy()` works", {
     f_fit,
     new_data = lung_pred,
     type = "survival",
-    time = c(100, 200)
+    eval_time = c(100, 200)
   )
   xy_pred_survival <- predict(
     xy_fit,
     new_data = lung_pred,
     type = "survival",
-    time = c(100, 200)
+    eval_time = c(100, 200)
   )
   expect_equal(f_pred_survival, xy_pred_survival)
 })