ecospat.maxentvarimport {ecospat} R Documentation

## Maxent Variable Importance

### Description

Calculate the importance of variables for Maxent in the same way Biomod does, by randomly permuting each predictor variable independently, and computing the associated reduction in predictive performance.

### Usage

ecospat.maxentvarimport (model, dfvar, nperm)

### Arguments

 model The name of the maxent model. dfvar A dataframe object with the environmental variables. nperm The number of permutations in the randomization process. The default is 5.

### Details

The calculation is made as biomod2 "variables_importance" function. It's more or less base on the same principle than randomForest variables importance algorithm. The principle is to shuffle a single variable of the given data. Make model prediction with this 'shuffled' data.set. Then we compute a simple correlation (Pearson's by default) between references predictions and the 'shuffled' one. The return score is 1-cor(pred_ref,pred_shuffled). The highest the value, the more influence the variable has on the model. A value of this 0 assumes no influence of that variable on the model. Note that this technique does not account for interactions between the variables.

### Value

a list which contains a data.frame containing variables importance scores for each permutation run.

### Author(s)

Blaise Petitpierre bpetitpierre@gmail.com

### Examples


library(dismo)
data('ecospat.testData')

# data for Soldanella alpina