Spatial Buffer Cross validation implemented by the blockCV package.

Note

However, the default settings allow to conduct a leave-one-out cross validation for two-class, multi-class and continuous response data, where each observation is one test set. For each test, all observations outside the buffer around the test observation are included in the training set.

The parameter spDataType = PB and addBG are designed for presence-background data in species distribution modelling. If spDataType = PB, test sets are only created for each presence observation (task\$positive). The option addBG = TRUE adds the background data inside the buffer to the corresponding test sets. For each test set, all observations outside the buffer around the test observation are included in the training set.

References

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2018). “blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models.” bioRxiv. doi: 10.1101/357798 .

See also

ResamplingSpCVDisc

Super class

mlr3::Resampling -> ResamplingSpCVBuffer

Active bindings

iters

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

Methods

Public methods

Inherited methods

Method new()

Create an "Environmental Block" resampling instance.

Usage

ResamplingSpCVBuffer$new(id = "spcv_buffer")

Arguments

id

character(1)
Identifier for the resampling strategy.


Method instantiate()

Materializes fixed training and test splits for a given task.

Usage

ResamplingSpCVBuffer$instantiate(task)

Arguments

task

Task
A task to instantiate.


Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingSpCVBuffer$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) { library(mlr3) task = tsk("ecuador") # Instantiate Resampling rcv = rsmp("spcv_buffer", theRange = 10000) rcv$instantiate(task) # Individual sets: rcv$train_set(1) rcv$test_set(1) intersect(rcv$train_set(1), rcv$test_set(1)) # Internal storage: # rcv$instance }
#> integer(0)