Variables importance calculation
This function will return a variable importance value for each variable involved within your model.
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 so far) |
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:
- mat: a data.frame containing variables importance
scores for each permutation run.
Damien Georges
xx <-
data.frame(
a = sample(c(0, 1), 100, replace = TRUE),
b = rnorm(100),
c = 1:100
)
mod <- glm(a ~ b + c, data = xx)
variables_importance(
model = mod,
data = xx[, c('b', 'c')],
method = "full_rand",
nb_rand = 3
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