rankTransform

internal

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', 'GLMMadaptive', 'spaMM', and 'brms'
(simple models); phylogenetic linear models from 'phylolm' (classes 'phylolm' and 'phyloglm');
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, temporal and phylogenetic autocorrelation.

Florian Hartig

DHARMa

Residual Diagnostics for Hierarchical (Multi-Level / Mixed)
Regression Models

Lukas Lohse

Melina de Souza Leite

Cosmina Werneke

rankTransform function

<dl><dt>x</dt>
<dd>predictor variable</dd></dl>

Arguments

Rank transform predictors — rankTransform

<dl>

<dt>x</dt>
<dd>predictor variable</dd>

</dl>

Rank transform predictors

rankTransform: Rank transform predictors

Description

Usage

Arguments