algoComp

algoComp-dataset

<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

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

algoComp-dataset function

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

algoComp-dataset: Performances SEM algorithm versus MCEM

Description

Usage

Arguments

Format

See Also