VSURF_interp: Interpretation step of VSURF

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/VSURF_interp.R

Description

Interpretation step aims to select all variables related to the response for interpretation purpose. This is the second step of the VSURF function. It is designed to be executed after the thresholding step VSURF_thres.

Usage

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VSURF_interp(x, ...)

## Default S3 method:
VSURF_interp(x, y, ntree = 2000, vars, nfor.interp = 25,
  nsd = 1, RFimplementation = "randomForest", parallel = FALSE,
  ncores = detectCores() - 1, clusterType = "PSOCK", ...)

## S3 method for class 'formula'
VSURF_interp(formula, data, ..., na.action = na.fail)

Arguments

x, formula

A data frame or a matrix of predictors, the columns represent the variables. Or a formula describing the model to be fitted.

...

others parameters to be passed on to the randomForest function (see ?randomForest for further information).

y

A response vector (must be a factor for classification problems and numeric for regression ones).

ntree

Number of trees in each forests grown. Standard parameter of randomForest.

vars

A vector of variable indices. Typically, indices of variables selected by thresholding step (see value varselect.thres of VSURF_thres function).

nfor.interp

Number of forests grown.

nsd

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

RFimplementation

Choice of the random forests implementation to use : "randomForest" (default) or "ranger".

parallel

A logical indicating if you want VSURF to run in parallel on multiple cores (default to FALSE).

ncores

Number of cores to use. Default is set to the number of cores detected by R minus 1.

clusterType

Type of the multiple cores cluster used to run VSURF in parallel. Must be chosen among "PSOCK" (default: SOCKET cluster available locally on all OS), "FORK" (local too, only available for Linux and Mac OS) and "MPI" (can be used on a remote cluster, which needs snow and Rmpi packages installed).

data

a data frame containing the variables in the model.

na.action

A function to specify the action to be taken if NAs are found. (NOTE: If given, this argument must be named, and as randomForest it is only used with the formula-type call.)

Details

nfor.interp embedded random forests models are grown, starting with the random forest build with only the most important variable and ending with all variables. Then, err.min the minimum mean out-of-bag (OOB) error rate of these models and its associated standard deviation sd.min are computed. Finally, the smallest model (and hence its corresponding variables) having a mean OOB error less than err.min + nsd * sd.min is selected.

Note that, the mtry parameter of randomForest is set to its default value (see randomForest) if nvm, the number of variables in the model, is not greater than the number of observations, while it is set to nvm/3 otherwise. This is to ensure quality of OOB error estimations along embedded RF models.

Value

An object of class VSURF_interp, which is a list with the following components:

varselect.interp

A vector of indices of selected variables.

err.interp

A vector of the mean OOB error rates of the embedded random forests models.

sd.min

The standard deviation of OOB error rates associated to the random forests model attaining the minimum mean OOB error rate.

num.varselect.interp

The number of selected variables.

varselect.thres

A vector of indexes of variables selected after "thresholding step", sorted according to their mean VI, in decreasing order.

nsd

Value of the parameter in the call.

comput.time

Computation time.

RFimplementation

The RF implementation used to run VSURF_interp.

ncores

The number of cores used to run VSURF_interp in parallel (NULL if VSURF_interp did not run in parallel).

clusterType

The type of the cluster used to run VSURF_interp in parallel (NULL if VSURF_interp did not run in parallel).

call

The original call to VSURF.

terms

Terms associated to the formula (only if formula-type call was used).

Author(s)

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

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, tune

Examples

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data(iris)
iris.thres <- VSURF_thres(iris[,1:4], iris[,5], ntree = 100, nfor.thres = 20)
iris.interp <- VSURF_interp(iris[,1:4], iris[,5],
  vars = iris.thres$varselect.thres, nfor.interp = 10)
iris.interp

## Not run: 
# A more interesting example with toys data (see \code{\link{toys}})
# (a few minutes to execute)
data(toys)
toys.thres <- VSURF_thres(toys$x, toys$y)
toys.interp <- VSURF_interp(toys$x, toys$y,
  vars = toys.thres$varselect.thres)
toys.interp
## End(Not run)

robingenuer/VSURF documentation built on April 14, 2018, 10:16 a.m.