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

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

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

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

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

Arguments

object

[Resampling]
mlr3 spatial resampling object of class ResamplingSpCVEnv or ResamplingRepeatedSpCVEnv.

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.

crs

[character]
EPSG code of the CRS for x and y axes.

...

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 ResamplingSpCVEnv or ResamplingRepeatedSpCVEnv.

See also

Examples

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) { library(mlr3) library(mlr3spatiotempcv) task = tsk("ecuador") resampling = rsmp("spcv_env", folds = 4, features = "dem") resampling$instantiate(task) autoplot(resampling, task, crs = 4326) + ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01)) autoplot(resampling, task, fold_id = 1, crs = 4326) autoplot(resampling, task, fold_id = c(1, 2), crs = 4326) * ggplot2::scale_x_continuous(breaks = seq(-79.085, -79.055, 0.01)) }