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AICcPermanova (version 0.0.2)

Model Selection of PERMANOVA Models Using AICc

Description

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) ().

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install.packages('AICcPermanova')

Monthly Downloads

89

Version

0.0.2

License

MIT + file LICENSE

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Maintainer

Derek Corcoran

Last Published

April 11th, 2023

Functions in AICcPermanova (0.0.2)

make_models

Create models with different combinations of variables
filter_vif

Filters out equations with high multicollinearity
VIF

Get Maximum Variance Inflation Factor (VIF) from a Model
select_models

Select models based on AICc and VIF.
fit_models

Fit PERMANOVA models and arrange by AICc
akaike_adjusted_rsq

Akaike-Adjusted R Squared Calculation with Model Averaging
AICc_permanova2

Calculate AICc for a permutational multivariate analysis of variance (PERMANOVA)