ecospat.maxentvarimport

0th

Percentile

Maxent Variable Importance

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.

Aliases
  • ecospat.maxentvarimport
Examples
## 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)
Documentation reproduced from package ecospat, version 2.0, License: GPL

Community examples

Looks like there are no examples yet.