benchmarkOverdispersion

dispersionValues

nRep

alpha

plot

whether to use parallel computations. Possible values are F, T (parallel cores set to number of cores in the computer -1), or an integer number for the number of cores that should be used

parallel

parameters to pass on to <code><a rd-options="" href="/link/simulateResiduals?package=DHARMa&version=0.1.6" data-mini-rdoc="DHARMa::simulateResiduals">simulateResiduals</a></code>

This function runs Power / Type I error simulations for overdispersion tests in DHARMa

The 'DHARMa' package uses a simulation-based approach to create
readily interpretable scaled (quantile) residuals for fitted generalized linear mixed
models. Currently supported are generalized linear mixed models from 'lme4'
(classes 'lmerMod', 'glmerMod'), generalized additive models ('gam' from 'mgcv'),
'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model
classes. Alternatively, 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

benchmarkOverdispersion function

parameters to pass on to <code><a rd-options='' href='simulateResiduals'>simulateResiduals</a></code>

benchmarkOverdispersion: Overdispersion benchmarks

Description

Usage

Arguments

Details

See Also