Perform Spatial Error Estimation and Variable Importance Assessment
runfolds
runreps
Add distance information to resampling objects
Resampling objects with repetition, i.e. sets of partitionings or boot...
Resampling objects such as partitionings or bootstrap samples
Alphanumeric tile names
Calculate mean nearest-neighbour distance between point datasets
Default error function
Identify small partitions that need to be fixed.
Partition the data for a (non-spatial) cross-validation
Partition the data for a stratified (non-spatial) cross-validation
Leave-one-disc-out cross-validation and leave-one-out cross-validation
Partition the data for a (non-spatial) leave-one-factor-out cross-vali...
Partition the data for a (non-spatial) k-fold cross-validation at the ...
Partition samples spatially using k-means clustering of the coordinate...
Partition the study area into rectangular tiles
Plot spatial resampling objects
remove_missing_levels
Non-spatial bootstrap resampling
Overlapping spatial block bootstrap using circular blocks
Bootstrap at an aggregated level
Spatial block bootstrap at the level of spatial k-means clusters
Spatial block bootstrap using rectangular blocks
Draw uniform random (sub)sample at the group level
Draw stratified random sample
Draw uniform random (sub)sample
Spatial Error Estimation and Variable Importance
Perform spatial error estimation and variable importance assessment
title Summary statistics for a resampling objects
Summary and print methods for sperrorest results
Summarize error statistics obtained by sperrorest
Summarize variable importance statistics obtained by sperrorest
Determine the names of neighbouring tiles in a rectangular pattern
transfer_parallel_output
Implements spatial error estimation and permutation-based variable importance measures for predictive models using spatial cross-validation and spatial block bootstrap.
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