mlr3spatiotempcv (version 0.1.1)

ResamplingRepeatedSpCVCoords: Repeated Spatial Cross Validation Resampling

Description

Spatial Cross validation following the "k-means" approach after Brenning 2012.

Super class

mlr3::Resampling -> ResamplingRepeatedSpCVCoords

Active bindings

iters

integer(1) Returns the number of resampling iterations, depending on the values stored in the param_set.

Methods

Public methods

• ResamplingRepeatedSpCVCoords$new()

• ResamplingRepeatedSpCVCoords$folds()

• ResamplingRepeatedSpCVCoords$repeats()

• ResamplingRepeatedSpCVCoords$instantiate()

• ResamplingRepeatedSpCVCoords$clone()

• mlr3::Resampling$format()

• mlr3::Resampling$help()

• mlr3::Resampling$print()

• mlr3::Resampling$test_set()

• mlr3::Resampling$train_set()

Method new()

Create an "coordinate-based" repeated resampling instance.

Usage

ResamplingRepeatedSpCVCoords$new(id = "repeated_spcv_coords")

Arguments

id

character(1) Identifier for the resampling strategy.

Method folds()

Translates iteration numbers to fold number.

Usage

ResamplingRepeatedSpCVCoords$folds(iters)

Arguments

iters

integer() Iteration number.

Method repeats()

Translates iteration numbers to repetition number.

Usage

ResamplingRepeatedSpCVCoords$repeats(iters)

Arguments

iters

integer() Iteration number.

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingRepeatedSpCVCoords$instantiate(task)

Arguments

task

Task A task to instantiate.

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingRepeatedSpCVCoords$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. 10.1109/igarss.2012.6352393.

Examples

# NOT RUN {
library(mlr3)
task = tsk("diplodia")

# Instantiate Resampling
rrcv = rsmp("repeated_spcv_coords", folds = 3, repeats = 5)
rrcv$instantiate(task)

# Individual sets:
rrcv$iters
rrcv$folds(1:6)
rrcv$repeats(1:6)

# Individual sets:
rrcv$train_set(1)
rrcv$test_set(1)
intersect(rrcv$train_set(1), rrcv$test_set(1))

# Internal storage:
rrcv$instance # table
# }