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.

1 |

A 200 x 28 matrix.

Overview : `clere-package`

Classes : `Clere`

, `Pacs`

Methods : `plot`

, `clusters`

, `predict`

, `summary`

Functions : `fitClere`

, `fitPacs`

Datasets : `numExpRealData`

, `numExpSimData`

, `algoComp`

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