varimplot-methods: A plot with variable importance score on X-axis and variable...

In RTIInternational/mobForest: Model Based Random Forest Analysis

Description

A plot with variable importance score on X-axis and variable name on Y-axis.

Usage

varimplot(object)

Arguments

object

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

References

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

See Also

get.varimp

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 = T,
        alpha = 0.05, bonferroni = T, minsplit = 25), data = BostonHousing,
    processors = 1, model = linearModel, seed = 1111)
 varimplot(rfout)

## End(Not run)

RTIInternational/mobForest documentation built on Aug. 3, 2019, 8:28 a.m.