fifer (version 1.1)

rfPred: Variable selection in Random Forest

Description

Variable selection for prediction purposes using Random Forest. See rfThresh for complete documentation.

Usage

rfPred(object, importance = "permutation", nfor.pred = 25, nmj = 1,
  outfile = "rfPred.file", named.file = "rfPredResults", ...)

Arguments

object

an object returned from rfInterp

importance

what importance measure should be used? Either "permutation" or "gini."

nfor.pred

number of forests to grow

nmj

a contant used for setting the threshold for variable selection. Higher values indicate a less stringent threshold.

outfile

The file location where the rfPred object should be stored. Defaults to storing it in rfPred.file in the default directory.

named.file

What should the rfPred object be named when saved? Defaults to "rfPredResults".

...

other arguments passed to cforest or randomForest

Value

varselect.pred

The variables selected for Prediction (sorted)

err.interp

The error at each stage of the stepwise variable inclusion.

mean.jump

The threshold for variable inclusion.

stepwise.error

The OOB error rate at each iteration of the stepwise procedure.

num.varselect.pred

The number of variables selected for prediction.

comput.time

Computation time of the procedure.

model

The final model, either a randomForest or cforest object.

See Also

rfInterp, rfThresh

Examples

## Not run: ------------------------------------
# data(iris); 
# data = iris; 
# formula = as.formula("Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width")
# thresh = rfThresh(formula, data=iris, nruns=2, importance="permutation"); 
# interp = rfInterp(thresh, importance="permutation");
# predic = rfPred(interp, importance="gini")
# predic
## ---------------------------------------------