hasWeights

For n/k models, weights can either represent the number of trials or actual prior weights on the likelihood. This function distinguishes the two cases and returns TRUE if the model has prior weights that are not the number of trials.

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

hasWeights function

<dl><dt>fittedModel</dt>
<dd>a fitted model</dd></dl>

Arguments

Check if a model has (problematic) prior weights — hasWeights

<dl>

<dt>fittedModel</dt>
<dd>a fitted model</dd>

</dl>

hasWeights: Check if a model has (problematic) prior weights

Description

Usage

Arguments