VSURF (version 1.2.0)

tune: Tuning of the thresholding and interpretation steps of VSURF

Description

This function allows to tune the "thresholding" and "interpretation step" of VSURF, without rerunning all computations.

Usage

tune(x, ...)
# S3 method for VSURF_thres
tune(x, nmin = 1, ...)
# S3 method for VSURF_interp
tune(x, nsd = 1, ...)

Value

An object with the same structure than the original output (from VSURF_thres or VSURF_interp).

Arguments

x

An object of class VSURF_thres or VSURF_interp, which is the result of the VSURF_thres or VSURF_interp function.

...

Not used.

nmin

Number of times the "minimum value" is multiplied to set threshold value. See details below.

nsd

Number of times the standard deviation of the minimum value of err.interp is multiplied. See details below.

Author

Robin Genuer, Jean-Michel Poggi and Christine Tuleau-Malot

Details

In VSURF_thres function, the actual threshold is performed like this: only variables with a mean VI larger than nmin * min.thres are kept. The function tune.VSURF_thres allows you to change the value of nmin (which multiply the estimated threshold value min.thres), without rerunning all computations. To get a softer threshold than default, choose a value of nmin less than 1, and to get a harder one, choose a value larger than 1.

In VSURF_interp function, the smallest model (and hence its corresponding variables) having a mean OOB error rate less than err.min + nsd * sd.min is selected. The function tune.VSURF_interp allows to change the value of nsd (which multiply the standard deviation of the minimum OOB error rate sd.min), without rerunning all computations. To get a larger model than default, choose a value of nsd less than 1, and to get a smaller one, choose a value larger than 1.

References

Genuer, R. and Poggi, J.M. and Tuleau-Malot, C. (2010), Variable selection using random forests, Pattern Recognition Letters 31(14), 2225-2236

Genuer, R. and Poggi, J.M. and Tuleau-Malot, C. (2015), VSURF: An R Package for Variable Selection Using Random Forests, The R Journal 7(2):19-33

See Also

VSURF, VSURF_thres, VSURF_interp

Examples

if (FALSE) {
data(iris)
iris.thres <- VSURF_thres(iris[,1:4], iris[,5], ntree = 100, nfor.thres = 20)
iris.thres.tuned <- tune(iris.thres, nmin = 10)
iris.thres.tuned
iris.interp <- VSURF_interp(iris[,1:4], iris[,5], vars = iris.thres$varselect.thres,
                            nfor.interp = 10)
iris.interp.tuned <- tune(iris.interp, nsd = 10)
iris.interp.tuned
}