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.
Tests Vignettes
Available Snapshots
This version of DHARMa can be found in the following snapshots:
