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' 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.

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Description file content

Package
DHARMa
Title
Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models
Version
0.2.6
Date
2019-11-24
Description
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' 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.
Depends
R (>= 3.0.2)
Imports
stats, graphics, utils, grDevices, parallel, doParallel, foreach, gap, qrnn, lmtest, ape, sfsmisc, MASS, lme4, mgcv, glmmTMB (>= 0.2.1), spaMM (>= 2.6.0)
Suggests
knitr, testthat
License
GPL (>= 3)
URL
BugReports
https://github.com/florianhartig/DHARMa/issues
LazyData
true
RoxygenNote
6.1.1
VignetteBuilder
knitr
Encoding
UTF-8
NeedsCompilation
no
Packaged
2019-11-26 15:28:30 UTC; florian
Author
Florian Hartig [aut, cre] ()
Maintainer
Florian Hartig
Repository
CRAN
Date/Publication
2019-11-26 20:50:02 UTC

install.packages('DHARMa')

0.2.6

19 days ago

http://florianhartig.github.io/DHARMa/

Florian Hartig

GPL (>= 3)

Depends on

R (>= 3.0.2)

Imports

stats, graphics, utils, grDevices, parallel, doParallel, foreach, gap, qrnn, lmtest, ape, sfsmisc, MASS, lme4, mgcv, glmmTMB (>= 0.2.1), spaMM (>= 2.6.0)

Suggests

knitr, testthat

Discussions