| mlr_resamplings_spcv_disc | R Documentation |
Spatial partitioning using circular test areas of one of more observations.
Optionally, a buffer around the test area can be used to exclude observations.
See the upstream implementation at sperrorest::partition_disc() and
Brenning (2012) for further information.
folds (integer(1))
Number of folds.
radius (numeric(1))
Radius of test area disc.
buffer (integer(1))
Radius around test area disc which is excluded from training or test set.
prob (integer(1))
Optional argument passed down to sample().
replace (logical(1))
Optional argument passed down to sample(). Sample with or without
replacement.
mlr3::Resampling -> ResamplingSpCVDisc
itersinteger(1)
Returns the number of resampling iterations, depending on the
values stored in the param_set.
new()Create a "Spatial 'Disc' resampling" resampling instance.
For a list of available arguments, please see sperrorest::partition_disc.
ResamplingSpCVDisc$new(id = "spcv_disc")
idcharacter(1)
Identifier for the resampling strategy.
instantiate()Materializes fixed training and test splits for a given task.
ResamplingSpCVDisc$instantiate(task)
taskmlr3::Task
A task to instantiate.
clone()The objects of this class are cloneable with this method.
ResamplingSpCVDisc$clone(deep = FALSE)
deepWhether to make a deep clone.
Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/igarss.2012.6352393")}.
library(mlr3)
task = tsk("ecuador")
# Instantiate Resampling
rcv = rsmp("spcv_disc", folds = 3L, radius = 200L, buffer = 200L)
rcv$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
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