The R Package groc for Generalized Regression on Orthogonal Components

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Martin Bilodeau, Pierre Lafaye de Micheaux, Smail Mahdi
Journal/Conference Name Journal of Statistical Software
Paper Category
Paper Abstract The R package groc for generalized regression on orthogonal components contains functions for the prediction of q responses using a set of p predictors. The primary building block is the grid algorithm used to search for components (projections of the data) which are most dependent on the response. The package offers flexibility in the choice of the dependence measure which can be user-defined. The components are found sequentially. A first component is obtained and a smooth fit produces residuals. Then, a second component orthogonal to the first is found which is most dependent on the residuals, and so on. The package can handle models with more than one response. A panoply of models can be achieved through package groc: robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, and classical or robust partial least squares models. Functions for predictions and cross-validation are available and helpful in model selection. The merit of a fit through cross-validation can be assessed with the predicted residual error sum of squares or the predicted residual error median absolute deviation which is more appropriate in the presence of outliers.
Date of publication 2015
Code Programming Language R
Comment

Copyright Researcher 2021