autoplot.ResamplingSpCVBuffer function

Visualization Functions for SpCV Buffer Methods.

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

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

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

Arguments

• object: [Resampling]

  mlr3 spatial resampling object of class ResamplingSpCVBuffer .

• 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.

• show_omitted: [logical]

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

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

• x: [Resampling]

  mlr3 spatial resampling object of class ResamplingSpCVBuffer .

Examples

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
  library(mlr3)
  library(mlr3spatiotempcv)
  task = tsk("ecuador")
  resampling = rsmp("spcv_buffer", theRange = 1000)
  resampling$instantiate(task)

  ## single fold
  autoplot(resampling, task, fold_id = 1) +
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))

  ## multiple folds
  autoplot(resampling, task, fold_id = c(1, 2)) *
    ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01))
}

See Also

• mlr3book chapter on "Spatial Analysis"
• autoplot.ResamplingSpCVBlock()
• autoplot.ResamplingSpCVCoords()
• autoplot.ResamplingSpCVEnv()
• autoplot.ResamplingCV()
• autoplot.ResamplingSptCVCstf()