Visualization Functions for Non-Spatial CV Methods.

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

Usage

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

# S3 method for class 'ResamplingRepeatedCV'
autoplot(
  object,
  task,
  fold_id = NULL,
  repeats_id = 1,
  plot_as_grid = TRUE,
  train_color = "#0072B5",
  test_color = "#E18727",
  sample_fold_n = NULL,
  ...
)

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

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

Arguments

object

[Resampling]
mlr3 spatial resampling object of class mlr3::ResamplingCV or mlr3::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 a list with of ggplot2 objects 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.

sample_fold_n

[integer]
Number of points in a random sample stratified over partitions. This argument aims to keep file sizes of resulting plots reasonable and reduce overplotting in dense datasets.

...

Passed to geom_sf(). Helpful for adjusting point sizes and shapes.

repeats_id

[numeric]
Repetition ID to plot.

x

[Resampling]
mlr3 spatial resampling object of class mlr3::ResamplingCV or mlr3::ResamplingRepeatedCV.

See also

• mlr3book chapter on "Spatial Analysis"

• autoplot.ResamplingSpCVBlock()

• autoplot.ResamplingSpCVBuffer()

• autoplot.ResamplingSpCVCoords()

• autoplot.ResamplingSpCVEnv()

• autoplot.ResamplingSpCVDisc()

• autoplot.ResamplingSpCVTiles()

• autoplot.ResamplingSptCVCstf()

Examples

# \donttest{
if (mlr3misc::require_namespaces(c("sf", "patchwork", "ggtext", "ggsci"), 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, fold_id = 1)
  autoplot(resampling, task, fold_id = c(1, 2)) *
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
}

# }