get.varimp-methods: Variable importance scores computed through random forest...

In mobForest: Model Based Random Forest Analysis

Description

Variable importance scores computed through random forest analysis

Usage

get.varimp(rf)

Arguments

rf	An object of class mobforest.output returned by mobforest.analysis()

References

Leo Breiman (2001). Random Forests. Machine Learning, 45(1), 5-32.

Examples

## Not run: 
library(mlbench)
set.seed(1111)
# Random Forest analysis of model based recursive partitioning load data
data("BostonHousing", package = "mlbench")
BostonHousing <- BostonHousing[1:90, c("rad", "tax", "crim", "medv", "lstat")]

# Recursive partitioning based on linear regression model medv ~ lstat with 3
# trees.  1 core/processor used. 
rfout <- mobforest.analysis(as.formula(medv ~ lstat), c("rad", "tax", "crim"),
    mobforest_controls = mobforest.control(ntree = 3, mtry = 2, replace = TRUE,
        alpha = 0.05, bonferroni = TRUE, minsplit = 25), data = BostonHousing,
    processors = 1, model = linearModel, seed = 1111)
# Returns a vector of variable importance scores
get.varimp(rfout)

## End(Not run)

mobForest documentation built on Aug. 1, 2019, 1:05 a.m.