Maxent Variable Importance

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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

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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

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## Not run: 
model <- get ("me.Achillea_millefolium", envir=ecospat.env)
dfvar <- ecospat.testData[4:8]
nperm <- 5
ecospat.maxentvarimport (model, cal, nperm)
## End(Not run)

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