testOverdispersion

a DHARMa object with simulated residuals created with <code><a rd-options="" href="/link/simulateResiduals?package=DHARMa&version=0.2.3" data-mini-rdoc="DHARMa::simulateResiduals">simulateResiduals</a></code>

simulationOutput

additional arguments to <code><a rd-options="" href="/link/testDispersion?package=DHARMa&version=0.2.3" data-mini-rdoc="DHARMa::testDispersion">testDispersion</a></code>

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.

Florian Hartig

DHARMa

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

testOverdispersion function

a DHARMa object with simulated residuals created with <code><a rd-options='' href='simulateResiduals'>simulateResiduals</a></code>

additional arguments to <code><a rd-options='' href='testDispersion'>testDispersion</a></code>

State of Data and AI Literacy Report 2025

testOverdispersion: Simulated overdisperstion tests

Description

Usage

Arguments

Details