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.
DHARMa is on CRAN. So, to install the latest CRAN release, just run
To get an overview about its functionality once the package is installed, run
library(DHARMa) ?DHARMa vignette("DHARMa", package="DHARMa")
The vignette can also be read online here. To cite the package, run
If you want to install the current (development) version from this repository, run
devtools::install_github(repo = "florianhartig/DHARMa", subdir = "DHARMa", dependencies = T, build_vignettes = T)
Below the status of the automatic Travis CI tests on the master branch (if this doesn load see here)
To install a specific (older) release, or a particular branch, decide for the version number that you want to install in https://github.com/florianhartig/DHARMa/releases (version numbering corresponds to CRAN, but there may be smaller releases that were not pushed to CRAN), or branch and run
devtools::install_github(repo = "florianhartig/DHARMa", subdir = "DHARMa", ref = "v0.0.2.1", dependencies = T, build_vignettes = T)
with the appropriate version number / branch as argument to ref.
A question by Catalina Gutiérrez Chacón provided me with the motivation write the first version of DHARMa. Thanks for useful suggestions to improve DHARMa by Jochen Fründ, Tomer J. Czaczkes, Luis Cayuela Delgado and Alexandre Courtiol and many other people that made comments on GitHub, Crossvalidated or via email.