Visualization Functions for Non-Spatial CV Methods.

Generic S3 plot() and autoplot() (ggplot2) methods.

# S3 method for ResamplingCV
autoplot(
  object,
  task,
  fold_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  crs = NULL,
  ...
)

# S3 method for ResamplingRepeatedCV
autoplot(
  object,
  task,
  fold_id = NULL,
  repeats_id = NULL,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  crs = NULL,
  ...
)

# S3 method for ResamplingCV
plot(x, ...)

# S3 method for ResamplingRepeatedCV
plot(x, ...)

Arguments

object	[Resampling] mlr3 spatial resampling object of class ResamplingCV or ResamplingRepeatedCV.
task	[TaskClassifST]/[TaskRegrST] mlr3 task object.
fold_id	[numeric] Fold IDs to plot.
plot_as_grid	[logical(1)] Should a gridded plot using via patchwork be created? If FALSE only a list with all ggplot2 resamplings is returned. Only applies if a numeric vector is passed to argument fold_id.
train_color	[character(1)] The color to use for the training set observations.
test_color	[character(1)] The color to use for the test set observations.
crs	[character] EPSG code of the CRS for x and y axes.
...	Not used.
repeats_id	[numeric] Repetition ID to plot.
x	[Resampling] mlr3 spatial resampling object of class ResamplingCV or ResamplingRepeatedCV.

Examples

if (mlr3misc::require_namespaces(c("sf", "patchwork", "ggtext"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  resampling = rsmp("cv")
  resampling$instantiate(task)

  autoplot(resampling, task) +
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
  autoplot(resampling, task, 1)
  autoplot(resampling, task, c(1, 2))
}
#> CRS not set, transforming to WGS84 (EPSG: 4326).
#> CRS not set, transforming to WGS84 (EPSG: 4326).
#> CRS not set, transforming to WGS84 (EPSG: 4326).
#> CRS not set, transforming to WGS84 (EPSG: 4326).