Description Usage Arguments References Examples

Variable importance scores computed through random forest analysis

1 |

`rf` |
An object of class |

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
## 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)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.