ensurePredictor

Ensures the existence of a valid predictor to plot residuals against

internal

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

ensurePredictor function

<dl><dt>simulationOutput</dt>
<dd>A DHARMa simulation output or an object that can be converted into a DHARMa simulation output.</dd>
<dt>predictor</dt>
<dd>An optional predictor. If no predictor is provided, will try to extract the fitted value.</dd></dl>

Arguments

Ensures the existence of a valid predictor to plot residuals against — ensurePredictor

<dl>

<dt>simulationOutput</dt>
<dd>A DHARMa simulation output or an object that can be converted into a DHARMa simulation output.</dd>


<dt>predictor</dt>
<dd>An optional predictor. If no predictor is provided, will try to extract the fitted value.</dd>

</dl>

ensurePredictor: Ensures the existence of a valid predictor to plot residuals against

Description

Usage

Arguments