rsample (version 0.0.2)

rolling_origin: Rolling Origin Forecast Resampling

Description

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.

Usage

rolling_origin(data, initial = 5, assess = 1, cumulative = TRUE,
  skip = 0, ...)

Arguments

data

A data frame.

initial

The number of samples used for analysis/modeling in the initial resample.

assess

The number of samples used for each assessment resample.

cumulative

A logical. Should the analysis resample grow beyond the size specified by `initial` at each resample?.

skip

A integer indicating how many (if any) resamples to skip to thin the total amount of data points in the analysis resample.

...

Not currently used.

Value

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.

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.

Examples

# NOT RUN {
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))
# }