numExpSimData

numExpSimData-dataset

<p>This dataset is a matrix of 200 rows and 28 colums. The columns can be
  grouped as three blocs of 9 (for each method compared: <code>LASSO</code>, <code>RIDGE</code>,
  Elastic net [<code>ELNET</code>], Stepwise variable selection [<code>STEP</code>], <code>CLERE</code>, CLERE
  sparse [<code>CLERE_s</code>], Spike and Slab [<code>SS</code>], <code>AVG</code> method and Pairwise
  Absolute Clustering and Sparsity [<code>PACS</code>]). Prediction errors (MSE),
  number of estimated parameters and time (seconds) to fit the data are
  compared.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. Each row corresponds
  to a simulated dataset on which all 9 methods were fitted. For more
  details, please refer to the package vignette.
  The R script used to create this dataset is clere/inst/doc/SimulatedDataExample.R.</p>

datasets

Clere

Implements an empirical Bayes approach for simultaneous variable clustering and regression. This version also (re)implements in C++ an R script proposed by Howard Bondell that fits the Pairwise Absolute Clustering and Sparsity (PACS) methodology (see Sharma et al (2013) <DOI:10.1080/15533174.2012.707849>).

Loic Yengo

clere

Simultaneous Variables Clustering and Regression

numExpSimData-dataset function

Format

Performances of 9 methods for dimension reduction on data simulated under the CLERE model — numExpSimData-dataset

Performances of 9 methods for dimension reduction on data simulated under the CLERE model

numExpSimData-dataset: Performances of 9 methods for dimension reduction on data simulated under the CLERE model

Description

Usage

Arguments

Format

See Also