plsRglm: Partial least squares linear and generalized linear regression for processing incomplete datasets by cross-validation and bootstrap techniques with R

F. Bertrand; M. Maumy-Bertrand

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arXiv · 1810.01005

plsRglm: Partial least squares linear and generalized linear regression for processing incomplete datasets by cross-validation and bootstrap techniques with R

F. Bertrand·M. Maumy-Bertrand

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Abstract

The aim of the plsRglm package is to deal with complete and incomplete datasets through several new techniques or, at least, some which were not yet implemented in R. Indeed, not only does it make available the extension of the PLS regression to the generalized linear regression models, but also bootstrap techniques, leave-one-out and repeated $k$-fold cross-validation. In addition, graphical displays help the user to assess the significance of the predictors when using bootstrap techniques. Biplots (Fig. 4) can be used to delve into the relationship between individuals and variables.

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BibTeX RIS

F. Bertrand, M. Maumy-Bertrand. 2018-10-01. plsRglm: Partial least squares linear and generalized linear regression for processing incomplete datasets by cross-validation and bootstrap techniques with R. https://arxiv.org/abs/1810.01005

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