select_models

This function selects models from a data frame based on the AICc and VIF values. Models with AICc greater than negative infinity and VIF less than or equal to 6 are considered. The difference in AICc values for each model is calculated with respect to the model with the minimum AICc. Models with a difference in AICc less than or equal to the specified delta_aicc value are selected.

Provides tools for model selection and model averaging of PerMANOVA
models using Akaike Information Criterion corrected for small sample sizes
(AICc) and Information Theoretic criteria principles. The package is built
around the PERMANOVA analysis from the 'vegan' package and provides a
streamlined workflow for generating and comparing models, obtaining model
weights, and summarizing results using model averaging approaches. The
methods implemented in this package are based on the practical information-
theoretic approach described by Burnham, K. P. and Anderson, D. R. (2002)
(<doi:10.1007/b97636>).

Derek Corcoran

AICcPermanova

Model Selection of PERMANOVA Models Using AICc

select_models function

<dl><dt>df</dt>
<dd>a data frame containing the models to select from.</dd>
<dt>delta_aicc</dt>
<dd>a numeric value specifying the maximum difference in AICc values allowed.</dd></dl>

Arguments

Select models based on AICc and VIF. — select_models

<dl>

<dt>df</dt>
<dd>a data frame containing the models to select from.</dd>


<dt>delta_aicc</dt>
<dd>a numeric value specifying the maximum difference in AICc values allowed.</dd>

</dl>

select_models: Select models based on AICc and VIF.

Description

Usage

Value

Arguments

Examples