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.
The DHARMa package creates readily interpretable residuals for generalized linear (mixed) models that are standardized to values between 0 and 1. This is achieved by a simulation-based approach, similar to the Bayesian p-value or the parametric bootstrap: 1) simulate new data from the fitted model 2) from this simulated data, calculate the cummulative density function 3) residual is the value of the empirical density function at the value of the observed data.
The package includes various functions that deal with issues such as
To get more information, install the package and run
A question by Catalina Gutiérrez Chacón provided me with the motivation write the first version of DHARMa. Thanks to Jochen Fründ, Tomer J. Czaczkes, Luis Cayuela Delgado and Alexandre Courtiol for useful suggestions to improve the software.
NOTE: for more news about the package, see https://github.com/florianhartig/DHARMa/releases
plotResiduals includes support for factors
updates to the help
including now the negative binomial models from MASS and lme4, as well as the possibility to create synthetic data from the negative binomial family
includes a createDHARMa function that allows using the plot functions of DHARMa also with externally created simualtions, for example for Bayesian predictive simulations