Package: DHARMa 0.4.6.1
DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models
The 'DHARMa' package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Currently supported are linear and generalized linear (mixed) models from 'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB' 'GLMMadaptive' and 'spaMM', generalized additive models ('gam' from 'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation.
Authors:
DHARMa_0.4.6.1.tar.gz
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DHARMa_0.4.6.1.tgz(r-4.4-any)DHARMa_0.4.6.1.tgz(r-4.3-any)
DHARMa_0.4.6.1.tar.gz(r-4.5-noble)DHARMa_0.4.6.1.tar.gz(r-4.4-noble)
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DHARMa.pdf |DHARMa.html✨
DHARMa/json (API)
NEWS
# Install 'DHARMa' in R: |
install.packages('DHARMa', repos = c('https://florianhartig.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/florianhartig/dharma/issues
- hurricanes - Hurricanes
glmmregressionregression-diagnosticsresidual
Last updated 14 hours agofrom:610b12bea6. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Sep 17 2024 |
R-4.5-win | OK | Sep 17 2024 |
R-4.5-linux | OK | Sep 17 2024 |
R-4.4-win | OK | Sep 17 2024 |
R-4.4-mac | OK | Sep 17 2024 |
R-4.3-win | OK | Sep 17 2024 |
R-4.3-mac | OK | Sep 17 2024 |
Exports:benchmarkRuntimecreateDatacreateDHARMagetFamilygetFittedgetFixedEffectsgetObservedResponsegetPearsonResidualsgetQuantilegetRandomStategetRefitgetResidualsgetSimulationsoutliersplotConventionalResidualsplotQQunifplotResidualsplotSimulatedResidualsrecalculateResidualsrunBenchmarkssimulateLRTsimulateResidualstestCategoricaltestDispersiontestGenerictestOutlierstestOverdispersiontestOverdispersionParametrictestPhylogeneticAutocorrelationtestQuantilestestResidualstestSimulatedResidualstestSpatialAutocorrelationtestTemporalAutocorrelationtestUniformitytestZeroInflationtransformQuantiles
Dependencies:apeaskpassbase64encbootbslibcachemclicodetoolscolorspacecommonmarkcpp11crayoncrosstalkcurldata.tabledigestdoParalleldplyrevaluatefansifarverfastmapfontawesomeforeachfsgapgap.datasetsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetshttpuvhttrisobanditeratorsjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelme4lmtestmagrittrMASSMatrixmemoisemgcvmimeminqamunsellnlmenloptropensslpillarpkgconfigplotlyplyrpromisespurrrqgamR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackrlangrmarkdownsassscalesshinysourcetoolsstringistringrsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxtableyamlzoo
DHARMa for Bayesians
Rendered fromDHARMaForBayesians.Rmd
usingknitr::rmarkdown
on Sep 17 2024.Last update: 2022-09-08
Started: 2021-01-26
DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models
Rendered fromDHARMa.Rmd
usingknitr::rmarkdown
on Sep 17 2024.Last update: 2024-09-11
Started: 2016-08-11