RaRank: Rank the features by selected percentages provided by the...

In RaSEn: Random Subspace Ensemble Classification and Variable Screening

Description

Rank the features by selected percentages provided by the output from RaScreen.

Usage

RaRank(object, selected.num = "all positive", iteration = object$iteration)

Arguments

object	output from RaScreen.
selected.num	the number of selected variables. User can either choose from the following popular options or input an positive integer no larger than the dimension. 'all positive': the number of variables with positive selected percentage. 'D': floor(D), where D is the maximum of ramdom subspace size. '1.5D': floor(1.5D). '2D': floor(2D). '3D': floor(3D). 'n/logn': floor(n/logn), where n is the sample size. '1.5n/logn': floor(1.5n/logn). '2n/logn': floor(2n/logn). '3n/logn': floor(3n/logn). 'n-1': the sample size n - 1. 'p': the dimension p.
iteration	indicates results from which iteration to use. It should be an positive integer. Default = the maximal interation round used by the output from RaScreen.

Value

Selected variables (indexes).

References

Tian, Y. and Feng, Y., 2021(a). RaSE: A variable screening framework via random subspace ensembles. Journal of the American Statistical Association, (just-accepted), pp.1-30.

Examples

## Not run: 
set.seed(0, kind = "L'Ecuyer-CMRG")
train.data <- RaModel("screening", 1, n = 100, p = 100)
xtrain <- train.data$x
ytrain <- train.data$y

# test RaSE screening with linear regression model and BIC
fit <- RaScreen(xtrain, ytrain, B1 = 100, B2 = 50, iteration = 0, model = 'lm',
cores = 2, criterion = 'bic')

# Select floor(n/logn) variables
RaRank(fit, selected.num = "n/logn")

## End(Not run)

RaSEn documentation built on Oct. 16, 2021, 9:06 a.m.