Package: sperrorest 3.0.5

Alexander Brenning

sperrorest: Perform Spatial Error Estimation and Variable Importance Assessment

Implements spatial error estimation and permutation-based variable importance measures for predictive models using spatial cross-validation and spatial block bootstrap.

Authors:Alexander Brenning [aut, cre], Patrick Schratz [aut], Tobias Herrmann [ctb]

sperrorest_3.0.5.tar.gz
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sperrorest.pdf |sperrorest.html✨
sperrorest/json (API)
NEWS

# Install 'sperrorest' in R:
install.packages('sperrorest', repos = c('https://giscience-fsu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/giscience-fsu/sperrorest/issues

Pkgdown site:https://giscience-fsu.github.io

On CRAN:

Conda:

cross-validationmachine-learningspatial-statisticsspatio-temporal-modelingstatistical-learning

6.42 score 19 stars 46 scripts 759 downloads 3 mentions 32 exports 31 dependencies

Last updated 2 years agofrom:b4d2a1426b. Checks:3 OK, 5 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 10 2025
R-4.5-winNOTEFeb 10 2025
R-4.5-macNOTEFeb 10 2025
R-4.5-linuxNOTEFeb 10 2025
R-4.4-winNOTEFeb 10 2025
R-4.4-macNOTEFeb 10 2025
R-4.3-winOKFeb 10 2025
R-4.3-macOKFeb 10 2025

Exports:add.distanceas.represamplingas.resamplingas.tilenamedataset_distanceerr_defaultget_small_tilesis_represamplingis.resamplingpartition_cvpartition_cv_stratpartition_discpartition_factorpartition_factor_cvpartition_kmeanspartition_loopartition_tilesremove_missing_levelsrepresampling_bootstraprepresampling_disc_bootstraprepresampling_factor_bootstraprepresampling_kmeans_bootstraprepresampling_tile_bootstrapresample_factorresample_strat_uniformresample_uniformrunfoldsrunrepssperroresttile_neighborstransfer_parallel_outputvalidate.resampling

Dependencies:bitopscaToolsclicodetoolsdigestdplyrfansifuturefuture.applygenericsglobalsgluegplotsgtoolsKernSmoothlifecyclelistenvmagrittrparallellypillarpkgconfigR6rlangROCRstringistringrtibbletidyselectutf8vctrswithr

Custom Predict and Model Functions

Patrick Schratz

Rendered fromcustom-pred-and-model-functions.Rmdusingknitr::rmarkdownon Feb 10 2025.

Last update: 2020-03-15
Started: 2017-06-11

Spatial Modeling Using Statistical Learning Techniques

Alexander Brenning, Patrick Schratz

Rendered fromspatial-modeling-use-case.Rmdusingknitr::rmarkdownon Feb 10 2025.

Last update: 2021-11-19
Started: 2017-06-11

Readme and manuals

Help Manual

Help pageTopics
Spatial Error Estimation and Variable Importancesperrorest-package
Add distance information to resampling objectsadd.distance add.distance.represampling add.distance.resampling
Resampling objects with repetition, i.e. sets of partitionings or bootstrap samplesas.represampling as.represampling.list as.represampling_list is_represampling print.represampling represampling
Resampling objects such as partitionings or bootstrap samplesas.resampling as.resampling.default as.resampling.factor as.resampling.list as.resampling_default as.resampling_list is.resampling print.resampling resampling validate.resampling
Alphanumeric tile namesas.character.tilename as.numeric.tilename as.tilename as.tilename.character as.tilename.numeric as.tilename_character as.tilename_numeric print.tilename tilename
Calculate mean nearest-neighbour distance between point datasetsdataset_distance
Default error functionerr_default
Identify small partitions that need to be fixed.get_small_tiles
Partition the data for a (non-spatial) cross-validationpartition_cv
Partition the data for a stratified (non-spatial) cross-validationpartition_cv_strat
Leave-one-disc-out cross-validation and leave-one-out cross-validationpartition_disc partition_loo
Partition the data for a (non-spatial) leave-one-factor-out cross-validation based on a given, fixed partitioningpartition_factor
Partition the data for a (non-spatial) k-fold cross-validation at the group levelpartition_factor_cv
Partition samples spatially using k-means clustering of the coordinatespartition_kmeans
Partition the study area into rectangular tilespartition_tiles
Plot spatial resampling objectsplot.represampling plot.resampling
Non-spatial bootstrap resamplingrepresampling_bootstrap
Overlapping spatial block bootstrap using circular blocksrepresampling_disc_bootstrap
Bootstrap at an aggregated levelrepresampling_factor_bootstrap
Spatial block bootstrap using rectangular blocksrepresampling_tile_bootstrap
Draw uniform random (sub)sample at the group levelresample_factor
Draw stratified random sampleresample_strat_uniform
Draw uniform random (sub)sampleresample_uniform
Perform spatial error estimation and variable importance assessmentsperrorest
title Summary statistics for a resampling objectssummary.represampling summary.resampling
Summarize error statistics obtained by sperrorestsummary.sperroresterror
Summarize variable importance statistics obtained by sperrorestsummary.sperrorestimportance
Summary and print methods for sperrorest resultsprint.sperrorest print.sperrorestbenchmarks print.sperroresterror print.sperrorestimportance print.sperrorestpackageversion print.sperrorestreperror summary.sperrorest summary.sperrorestreperror
Determine the names of neighbouring tiles in a rectangular patterntile_neighbors