Description Usage Arguments Details Value Author(s) See Also Examples
This function will return a variable importance value for each variable involved within your model.
1 | variables_importance(model, data, method="full_rand", nb_rand=1, ...)
|
model |
the model you want to study variables importance (one of the models supported within biomod2, ensemble models are also supported) |
data |
the |
method |
the randomisation method (only 'full_rand' available yet) |
nb_rand |
the number of permutation done for each variable |
... |
additional args (not implemented yet) |
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.
a list
of class "BIOMOD_variables_importances" which contains:
mata data.frame
containing variables importance scores for each permutation run.
Damien Georges
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