mlr3spatiotempcv (version 1.0.0)

ResamplingSpCVCoords: (sperrorest) Coordinate-based k-means clustering

Description

partition_cv creates a represampling object for length(repetition)-repeated nfold-fold cross-validation.

mlr3spatiotempcv notes

The 'Description' and 'Details' fields are inherited from the respective upstream function.

For a list of available arguments, please see sperrorest::partition_cv.

Super class

mlr3::Resampling -> ResamplingSpCVCoords

Active bindings

iters

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

Methods

Public methods

• ResamplingSpCVCoords$new()

• ResamplingSpCVCoords$instantiate()

• ResamplingSpCVCoords$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.

For a list of available arguments, please see sperrorest::partition_cv.

Usage

ResamplingSpCVCoords$new(id = "spcv_coords")

Arguments

id

character(1) Identifier for the resampling strategy.

Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVCoords$instantiate(task)

Arguments

task

Task A task to instantiate.

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVCoords$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

This function does not actually perform a cross-validation or partition the data set itself; it simply creates a data structure containing the indices of training and test samples.

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("ecuador")

# Instantiate Resampling
rcv = rsmp("spcv_coords", folds = 5)
rcv$instantiate(task)

# Individual sets:
rcv$train_set(1)
rcv$test_set(1)
# check that no obs are in both sets
intersect(rcv$train_set(1), rcv$test_set(1)) # good!

# Internal storage:
rcv$instance # table
# }