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 generalized linear mixed models from 'lme4' (classes 'lmerMod', 'glmerMod'), generalized additive models ('gam' from 'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model classes. Alternatively, 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.

AuthorFlorian Hartig [aut, cre]
Date of publication2016-12-12 00:48:23
MaintainerFlorian Hartig <florian.hartig@biologie.uni-regensburg.de>
LicenseGPL (>= 3)
Version0.1.3
http://florianhartig.github.io/DHARMa/

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Files in this package

DHARMa
DHARMa/inst
DHARMa/inst/examples
DHARMa/inst/examples/simulateResidualsHelp.R
DHARMa/inst/examples/createDataHelp.R
DHARMa/inst/examples/benchmarkUniformityHelp.R
DHARMa/inst/doc
DHARMa/inst/doc/DHARMa.html
DHARMa/inst/doc/DHARMa.R
DHARMa/inst/doc/DHARMa.Rmd
DHARMa/tests
DHARMa/tests/manualTests
DHARMa/tests/manualTests/PowerBias.Rmd
DHARMa/tests/testthat.R
DHARMa/tests/testthat
DHARMa/tests/testthat/Rplots.pdf
DHARMa/tests/testthat/testModelTypes.R
DHARMa/NAMESPACE
DHARMa/NEWS
DHARMa/R
DHARMa/R/benchmarkPvalues.R DHARMa/R/plotResiduals.R DHARMa/R/benchmarkOverdispersion.R DHARMa/R/testsParametric.R DHARMa/R/DHARMa.R DHARMa/R/benchmarkUniformity.R DHARMa/R/testsResiduals.R DHARMa/R/simulateResiduals.R DHARMa/R/compatibility.R DHARMa/R/createData.R
DHARMa/vignettes
DHARMa/vignettes/Overdispersion.png
DHARMa/vignettes/ECDFmotivation.pdf
DHARMa/vignettes/DHARMa.Rmd
DHARMa/vignettes/ECDFmotivation.png
DHARMa/README.md
DHARMa/MD5
DHARMa/build
DHARMa/build/vignette.rds
DHARMa/DESCRIPTION
DHARMa/man
DHARMa/man/DHARMa.Rd DHARMa/man/testZeroInflation.Rd DHARMa/man/benchmarkOverdispersion.Rd DHARMa/man/testUniformity.Rd DHARMa/man/createDHARMa.Rd DHARMa/man/fitted.gam.Rd DHARMa/man/print.DHARMa.Rd DHARMa/man/benchmarkP.Rd DHARMa/man/simulateResiduals.Rd DHARMa/man/plot.DHARMa.Rd DHARMa/man/testOverdispersionParametric.Rd DHARMa/man/plotSimulatedResiduals.Rd DHARMa/man/benchmarkUniformity.Rd DHARMa/man/testTemporalAutocorrelation.Rd DHARMa/man/createData.Rd DHARMa/man/testSpatialAutocorrelation.Rd DHARMa/man/plotConventionalResiduals.Rd DHARMa/man/if-predictor-is-a-factor-a-boxplot-will-be-plotted-instead-of-a-scatter-plot..Rd DHARMa/man/testOverdispersion.Rd DHARMa/man/testSimulatedResiduals.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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