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
iters
integer(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$new(id = "spcv_disc")
id
character(1)
Identifier for the resampling strategy.
instantiate()
Materializes fixed training and test splits for a given task.
ResamplingSpCVDisc$instantiate(task)
task
Task
A task to instantiate.
clone()
The objects of this class are cloneable with this method.
ResamplingSpCVDisc$clone(deep = FALSE)
deep
Whether to make a deep clone.
Brenning A (2012). “Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.” In 2012 IEEE International Geoscience and Remote Sensing Symposium. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/igarss.2012.6352393")}.
library(mlr3)
task = tsk("ecuador")
# Instantiate Resampling
rcv = rsmp("spcv_disc", folds = 3L, radius = 200L, buffer = 200L)
rcv$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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