model_overdisp

Adds an observational level random effect to a model, if applicable.

internal

Partitioning the R2 of GLMMs into variation explained by each
predictor and combination of predictors using semi-partial (part) R2 and
inclusive R2. Methods are based on the R2 for GLMMs described in
Nakagawa & Schielzeth (2013) and Nakagawa, Johnson & Schielzeth (2017).

Martin A Stoffel

partR2

Partitioning R2 in GLMMs

Martin A. Stoffel

Shinichi Nakagawa

Holger Schielzeth

model_overdisp function

<dl><dt>mod</dt>
<dd>merMod object.</dd>
<dt>dat</dt>
<dd>The underlying data.frame</dd></dl>

Arguments

Adds an observational level random effect to a model, if applicable. — model_overdisp

<dl>

<dt>mod</dt>
<dd>merMod object.</dd>


<dt>dat</dt>
<dd>The underlying data.frame</dd>

</dl>

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model_overdisp: Adds an observational level random effect to a model, if applicable.

Description

Usage

Value

Arguments