# get.varimp

From mobForest v1.3.1
by Kasey Jones

##### Variable importance scores computed through random forest analysis

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)
# }
# NOT RUN {
# }
```

*Documentation reproduced from package mobForest, version 1.3.1, License: GPL (>= 2)*

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