#' Rolling Origin Forecast Resampling
#'
#' This resampling method is useful when the data set has a strong time component. The resamples are not random and contain data points that are consecutive values. The function assumes that the original data set are sorted in time order.
#'
#' @details
#' The main options, `initial` and `assess`, control the number of data points from the original data that are in the analysis and assessment set, respectively. When `cumulative = TRUE`, the analysis set will grow as resampling continues while the assessment set size will always remain static.
#' `skip` enables the function to not use every data point in the resamples. When `skip = 0`, the resampling data sets will increment by one position. Suppose that the rows of a data set are consecutive days. Using `skip = 6` will make the analysis data set operate on *weeks* instead of days. The assessment set size is not affected by this option.
#' @inheritParams vfold_cv
#' @param initial The number of samples used for analysis/modeling in the initial resample.
#' @param assess The number of samples used for each assessment resample.
#' @param cumulative A logical. Should the analysis resample grow beyond the size specified by `initial` at each resample?.
#' @param skip A integer indicating how many (if any) resamples to skip to thin the total amount of data points in the analysis resample.
#' @export
#' @return An tibble with classes `rolling_origin`, `rset`, `tbl_df`, `tbl`, and `data.frame`. The results include a column for the data split objects and a column called `id` that has a character string with the resample identifier.
#' @examples
#' set.seed(1131)
#' ex_data <- data.frame(row = 1:20, some_var = rnorm(20))
#' dim(rolling_origin(ex_data))
#' dim(rolling_origin(ex_data, skip = 2))
#' dim(rolling_origin(ex_data, skip = 2, cumulative = FALSE))
#' @export
rolling_origin <- function(data, initial = 5, assess = 1,
cumulative = TRUE, skip = 0, ...) {
n <- nrow(data)
if (n <= initial + assess)
stop("There should be at least ",
initial + assess,
" nrows in `data`",
call. = FALSE)
stops <- seq(initial, (n - assess), by = skip + 1)
starts <- if (!cumulative)
stops - initial + 1
else
starts <- rep(1, length(stops))
in_ind <- mapply(seq, starts, stops, SIMPLIFY = FALSE)
out_ind <-
mapply(seq, stops + 1, stops + assess, SIMPLIFY = FALSE)
indices <- mapply(merge_lists, in_ind, out_ind, SIMPLIFY = FALSE)
split_objs <-
purrr::map(indices, make_splits, data = data, class = "rof_split")
split_objs <- list(splits = split_objs,
id = names0(length(split_objs), "Slice"))
roll_att <- list(initial = initial,
assess = assess,
cumulative = cumulative,
skip = skip)
new_rset(splits = split_objs$splits,
ids = split_objs$id,
attrib = roll_att,
subclass = c("rolling_origin", "rset"))
}
#' @export
print.rolling_origin <- function(x, ...) {
cat("#", pretty(x), "\n")
class(x) <- class(x)[!(class(x) %in% c("rolling_origin", "rset"))]
print(x)
}
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