R: Performances of 9 methods for dimension reduction on data...
numExpSimData-dataset
R Documentation
Performances of 9 methods for dimension reduction on data simulated under the CLERE model
Description
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: 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]). 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.