algoComp

<p>This data contains four matrices corresponding to four performance indictors
used to compare SEM algorithm and three versions of the MCEM algorithm
(MCEMA: with 5 MC interations; MCEMB: with 25 MC iterations and MCEMC: 125
MC iterations) as described in the package vignette. The first matrix
<code>Pred</code> contains prediction errors; matrix <code>Bias</code> contains the bias
over all model parameters, matrix <code>Time</code> contains execution times for
the four methods and matrix <code>Liks</code> the log-likelihood reached by each
method. These data were used to generate the Table 1. in the package
vignette. For more details, please refer to the package vignette.  The R
script used to create this dataset is
clere/inst/doc/SEM_vs_MCEM_simulations.R.</p>

datasets

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

Micka<c3><ab>l Canouil

algoComp function

<p>A list containing four 200 x 4/5 matrices.</p>

algoComp: Performances SEM algorithm versus MCEM

Description

Usage

Arguments

Format

See Also