autoplot.ResamplingSpCVTiles function

Visualization Functions for SpCV Tiles Method.

Generic S3 plot() and autoplot() (ggplot2) methods to visualize mlr3 spatiotemporal resampling objects.

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

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

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

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

Arguments

• object: [Resampling]

  mlr3 spatial resampling object of class ResamplingSpCVBlock or ResamplingRepeatedSpCVBlock .

• task: [TaskClassifST]/[TaskRegrST]

  mlr3 task object.

• fold_id: [numeric]

  Fold IDs to plot.

• plot_as_grid: [logical(1)]

  Should a gridded plot using via list("patchwork") be created? If FALSE

  a list with of list("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.

• repeats_id: [numeric]

  Repetition ID to plot.

• show_omitted: [logical]

  Whether to show points not used in train or test set for the current fold.

• 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.

• x: [Resampling]

  mlr3 spatial resampling object. One of class ResamplingSpCVBuffer , ResamplingSpCVBlock , ResamplingSpCVCoords , ResamplingSpCVEnv .

Details

Specific combinations of arguments of "spcv_tiles" remove some observations, hence show_omitted has an effect in some cases.

Examples

if (mlr3misc::require_namespaces(c("sf", "sperrorest"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  resampling = rsmp("spcv_tiles",
    nsplit = c(4L, 3L), reassign = FALSE)
  resampling$instantiate(task)

  autoplot(resampling, task,
    fold_id = 1,
    show_omitted = TRUE, size = 0.7) *
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
}

See Also

• mlr3book chapter on "Spatial Analysis"
• Vignette Spatiotemporal Visualization.
• autoplot.ResamplingSpCVBlock()
• autoplot.ResamplingSpCVBuffer()
• autoplot.ResamplingSpCVCoords()
• autoplot.ResamplingSpCVDisc()
• autoplot.ResamplingSpCVEnv()
• autoplot.ResamplingCV()