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pls (version 2.6-0)

Partial Least Squares and Principal Component Regression

Description

Multivariate regression methods Partial Least Squares Regression (PLSR), Principal Component Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).

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Version

Install

install.packages('pls')

Monthly Downloads

28,707

Version

2.6-0

License

GPL-2

Maintainer

Bj�rn-Helge Mevik

Last Published

December 18th, 2016

Functions in pls (2.6-0)

delete.intercept

Delete intercept from model matrix

Kernel PLS (Dayal and MacGregor)

Plot Regression Coefficients of PLSR and PCR models

Cross-validation of PLSR and PCR models

Generate segments for cross-validation

Biplots of PLSR and PCR Models.

Jackknife approximate t tests of regression coefficients

Octane numbers and NIR spectra of gasoline

Extract Information From a Fitted PLSR or PCR Model

CPPLS (Indahl et al.)

Plot Method for MVR objects

Multiplicative Scatter Correction

Sensory and physico-chemical data of olive oils

Cross-validation

Partial Least Squares and Principal Component Regression

NIR measurements and oil types of mayonnaise

Orthogonal scores PLSR

Set or return options for the pls package

Adjust for Missing Values

MSEP, RMSEP and R2 of PLSR and PCR models

Standardization of Data Matrices

Plots of Scores, Loadings and Correlation Loadings

Principal Component Regression

Sijmen de Jong's SIMPLS

Prediction Plots

Validation Plots

Extract Scores and Loadings from PLSR and PCR Models

Predict Method for PLSR and PCR

Suggestions for the optimal number of components in PCR and PLSR models

Summary and Print Methods for PLSR and PCR objects

NIR spectra and density measurements of PET yarns

widekernelpls.fit

Wide Kernel PLS (R<e4>nnar et al.)

Jackknife Variance Estimates of Regression Coefficients