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_3.0.5.tgz(r-4.4-any)sperrorest_3.0.5.tgz(r-4.3-any)
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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'))

Peer review:

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

Datasets:

    On CRAN:

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

    6.39 score 18 stars 45 scripts 469 downloads 3 mentions 32 exports 31 dependencies

    Last updated 1 years agofrom:b4d2a1426b. Checks:OK: 3 NOTE: 4. Indexed: yes.

    TargetResultDate
    Doc / VignettesOKNov 12 2024
    R-4.5-winNOTENov 12 2024
    R-4.5-linuxNOTENov 12 2024
    R-4.4-winNOTENov 12 2024
    R-4.4-macNOTENov 12 2024
    R-4.3-winOKNov 12 2024
    R-4.3-macOKNov 12 2024

    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 Nov 12 2024.

    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 Nov 12 2024.

    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