make_models {AICcPermanova}

Create models with different combinations of variables

Description

Generates all possible linear models for a given set of predictor variables using the distance matrix as a response variable. The function allows for the user to specify the maximum number of variables in a model, which can be useful in cases where there are many predictors. The output is a data frame containing all the possible models, which can be passed to the fit_models function for fitting using a PERMANOVA approach.

Usage

make_models(vars, ncores = 2, k = NULL, verbose = TRUE)

Arguments

vars

A character vector of variables to use for modeling

ncores

An integer specifying the number of cores to use for parallel processing

k

maximum number of variables in a model, default is NULL

verbose

logical, defaults TRUE, sends messages about processing times

Value

A data frame containing all the possible linear permanova models

References

Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecology, 26(1), 32-46.

Examples

make_models(vars = c("A", "B", "C", "D"),
            ncores = 2, verbose = FALSE)

# using k as a way to limit number of variables
make_models(vars = c("A", "B", "C", "D"),
            ncores = 2, k = 2, verbose = FALSE)