fifer (version 1.1)

rfInterp: Variable Selection with Random Forest

Description

Find best variables using Random Forest (Interpretation Step). Find complete documentation at rfThresh

Usage

rfInterp(object, nruns = 20, nsd = 1, importance = "permutation", ...)

Arguments

object

a rfThresh object

nruns

how many forests should be grown?

nsd

defaults to one.

importance

method of calculating importance (permutation or gini)

...

other arguments passed to cforest or randomForest

Value

varselect.interp

The variables selected for Interpretation (sorted)

err.interp

The error at each stage of the stepwise variable inclusion.

sd.min

The standard deviation of the minimum fitted value.

num.varselect.interp

The number of variables selected for interpretation.

comput.time

Computation time of the procedure.

data

The dataset used for fitting the RF algorithm

formula

The formula of all variables included after the interpretation step.

See Also

rfInterp, rfPred

Examples

	#### do threshold step
## Not run: ------------------------------------
# data(iris); 
# data = iris; 
# formula = as.formula("Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width")
# object = rfThresh(formula, data=iris, nruns=2, importance="gini"); 
# #### run interpretation step
# rfInterp(object, nruns=10, importance="permutation")
## ---------------------------------------------