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The pls Package: Principal Component and Partial Least Squares Regression in R

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  • Björn-Helge Mevik
  • Ron Wehrens
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    Abstract

    The pls package implements principal component regression (PCR) and partial least squares regression (PLSR) in R (R Development Core Team 2006b), and is freely available from the Comprehensive R Archive Network (CRAN), licensed under the GNU General Public License (GPL). The user interface is modelled after the traditional formula interface, as exemplified by lm. This was done so that people used to R would not have to learn yet another interface, and also because we believe the formula interface is a good way of working interactively with models. It thus has methods for generic functions like predict, update and coef. It also has more specialised functions like scores, loadings and RMSEP, and a exible crossvalidation system. Visual inspection and assessment is important in chemometrics, and the pls package has a number of plot functions for plotting scores, loadings, predictions, coefficients and RMSEP estimates. The package implements PCR and several algorithms for PLSR. The design is modular, so that it should be easy to use the underlying algorithms in other functions. It is our hope that the package will serve well both for interactive data analysis and as a building block for other functions or packages using PLSR or PCR. We will here describe the package and how it is used for data analysis, as well as how it can be used as a part of other packages. Also included is a section about formulas and data frames, for people not used to the R modelling idioms.

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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 18 ()
    Issue (Month): i02 ()
    Pages:

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    Handle: RePEc:jss:jstsof:18:i02

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    Web page: http://www.jstatsoft.org/

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    Cited by:
    1. Ignacio González & Sébastien Déjean & Pascal G. P. Martin & Alain Baccini, . "CCA: An R Package to Extend Canonical Correlation Analysis," Journal of Statistical Software, American Statistical Association, vol. 23(i12).

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