Performances of 9 methods for dimension reduction applied to 2 published real dataset

Description

This data contains two matrices: one for the Prostate dataset (from R package lasso2) and the other for the eyedata dataset (from R package flare). Each matrix has 5 rows and 28 colums. The columns can be grouped as three blocs of 9 (for each method compared: LASSO, RIDGE, Elastic net [ELNET], Stepwise variable selection [STEP], CLERE, CLERE sparse [CLERE_s], Spike and Slab [SS], AVG method and Pairwise Absolute Clustering and Sparsity [PACS]). The 1st 9 (1:9) contain prediction error obtained by 5-fold cross validation using 10 random permutation of the covariate matrix. The 2nd 9 columns (10:18) contain the number of parameters estimated for each method. The 3rd 9 columns are times in seconds measured for fitting each methods. The 28 column is the seed utilized for generating random numbers in these analyses. For more details, please refer the package vignette. The R script used to create this dataset is clere/inst/doc/RealDataExample.R.

Usage

 data(numExpRealData)

Format

A list containing two matrices: one for the Prostate dataset (from R package lasso2) and the other for the Eye dataset (from R package flare)

Author(s)

Loic Yengo loic.yengol@gmail.com

See Also

Overview : clere-package
Classes : Clere, Pacs
Functions : fitClere, fitPacs
Datasets : numExpSimData, numExpRealData, algoComp

Results