Graphical summary for MCEM/SEM-Gibbs estimation. This function represents the course of the model parameters in view of the iterations of the estimation algorithms implemented in fitClere.
The methodology consists in creating clusters of variables involved in a high dimensional linear regression model so as to reduce the dimensionality. A model-based approach is proposed and fitted using a Stochastic EM-Gibbs algorithm (SEM-Gibbs).
This function implements the PACS (Pairwise Absolute Clustering and Sparsity) methodology of Sharma DB et al. (2013). This methodology proposes to estimate the regression coefficients by solving a penalized least squares problem. It imposes a constraint on Beta (the vector of regression coefficients) that is a weighted combination of the L1 norm and the pairwise L-infinity norm. Upper-bounding the pairwise L-infinity norm enforces the covariates to have close coefficients. When the constraint is strong enough, closeness translates into equality achieving thus a grouping property. For PACS, no software was available. Only an R script was released on Bondell's webpage (http://www4.stat.ncsu.edu/~bondell/Software/PACS/PACS.R.r). Since this R script was running very slowly, we decided to reimplement it in C++ and interfaced it with the present R package clere. This corresponds to the option type=1 in Bondell's script.
● Data Source:
CranContrib
● Keywords: Clere, fitClere, fitPacs, function
● Alias: fitPacs
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This function makes prediction using a fitted model and a new matrix of design. It returns a vector of predicted values of size equal to the number of rows of matrix newx.
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