make_models {AICcPermanova} | R Documentation |

## 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)
```

*AICcPermanova*version 0.0.2 Index]