Visualization Functions for SpCV Buffer Methods.

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

Usage

# 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 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.

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.

See also

• mlr3book chapter on "Spatial Analysis"

• autoplot.ResamplingSpCVBlock()

• autoplot.ResamplingSpCVCoords()

• autoplot.ResamplingSpCVEnv()

• autoplot.ResamplingCV()

• autoplot.ResamplingSptCVCstf()

Examples

# \donttest{
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))
}

# }