numExpSimData: Performances of 9 methods for dimension reduction on data...
In clere: Simultaneous Variables Clustering and Regression

Description Usage Format See Also

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.