testOverdispersion

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', 'GLMMadaptive', and 'spaMM'; 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, phylogenetic and temporal autocorrelation.

Florian Hartig

DHARMa

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

Lukas Lohse

Melina de Souza leite

testOverdispersion function

<dl><dt>simulationOutput</dt>
<dd>an object of class DHARMa with simulated quantile residuals, either created via simulateResiduals or by createDHARMa for simulations created outside DHARMa</dd>
<dt>...</dt>
<dd>additional arguments to testDispersion</dd></dl>

Arguments

Simulated overdisperstion tests — testOverdispersion

<dl>

<dt>simulationOutput</dt>
<dd>an object of class DHARMa with simulated quantile residuals, either created via simulateResiduals or by createDHARMa for simulations created outside DHARMa</dd>


<dt>...</dt>
<dd>additional arguments to testDispersion</dd>

</dl>

testOverdispersion: Simulated overdisperstion tests

Description

Usage

Arguments

Details