From bdda1c5e362f54179583f2d395593b382cf1abc8 Mon Sep 17 00:00:00 2001 From: nmercadeb Date: Thu, 15 Aug 2024 12:15:54 -0700 Subject: [PATCH] deprecate rolling_origin, issue #448 --- R/rolling_origin.R | 6 ++++++ vignettes/Common_Patterns.Rmd | 8 -------- 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/R/rolling_origin.R b/R/rolling_origin.R index 1a49993b..96352293 100644 --- a/R/rolling_origin.R +++ b/R/rolling_origin.R @@ -59,6 +59,12 @@ #' @export rolling_origin <- function(data, initial = 5, assess = 1, cumulative = TRUE, skip = 0, lag = 0, ...) { + + lifecycle::signal_stage( + stage = "superseded", what = "rolling_origin()", + with = I("sliding_window(), sliding_index() and sliding_period()") + ) + check_dots_empty() n <- nrow(data) diff --git a/vignettes/Common_Patterns.Rmd b/vignettes/Common_Patterns.Rmd index 039cf0ac..a69a0c7d 100644 --- a/vignettes/Common_Patterns.Rmd +++ b/vignettes/Common_Patterns.Rmd @@ -223,11 +223,3 @@ sliding_period(Chicago, date, "year") %>% head(2) ``` -All of these functions produce analysis sets of the same size, with the start and end of the analysis set "sliding" down your data frame. If you'd rather have your analysis set get progressively larger, so that you're predicting new data based upon a growing set of older observations, you can use the `rolling_origin()` function: - -```{r} -rolling_origin(Chicago) %>% - head(2) -``` - -Note that all of these time-based resampling functions are deterministic: unlike the rest of the package, running these functions repeatedly under different random seeds will always return the same results.