Package index • pls

Model fitting

Functions for fitting PLSR and PCR models.

mvr() plsr() pcr() cppls() nipals() nipalspcr(): Partial Least Squares and Principal Component Regression
mvrValstats() R2() MSEP() RMSEP(): MSEP, RMSEP and R2 of PLSR and PCR models

Functions for summarizing fitted models.

print(<mvr>) summary(<mvr>) print(<mvrVal>) as.data.frame(<mvrVal>): Summary and Print Methods for PLSR and PCR objects
coefplot(): Plot Regression Coefficients of PLSR and PCR models
predplot() predplotXy(): Prediction Plots

Functions for plotting fitted models.

plot(<mvr>): Plot Method for MVR objects
scoreplot() plot(<scores>) loadingplot() plot(<loadings>) corrplot(): Plots of Scores, Loadings and Correlation Loadings
biplot(<mvr>): Biplots of PLSR and PCR Models.
predplot() predplotXy(): Prediction Plots
validationplot() plot(<mvrVal>): Validation Plots

Functions for extracting information from fitted models.

coef(<mvr>) fitted(<mvr>) residuals(<mvr>) model.frame(<mvr>) model.matrix(<mvr>) respnames() prednames() compnames() explvar(): Extract Information From a Fitted PLSR or PCR Model
loadings() scores() Yscores() loading.weights() Yloadings(): Extract Scores and Loadings from PLSR and PCR Models
predict(<mvr>): Predict Method for PLSR and PCR
vcov(<mvr>): Calculate Variance-Covariance Matrix for a Fitted Model Object
selectNcomp(): Suggestions for the optimal number of components in PCR and PLSR models
pls.options(): Set or return options for the pls package

Functions for validating fitted models.

crossval(): Cross-validation of PLSR and PCR models
mvrCv(): Cross-validation
cvsegments(): Generate segments for cross-validation
fac2seg(): Factor to Segments
mvrValstats() R2() MSEP() RMSEP(): MSEP, RMSEP and R2 of PLSR and PCR models

Functions for testing fitted models.

jack.test() print(<jacktest>): Jackknife approximate t tests of regression coefficients
var.jack(): Jackknife Variance Estimates of Regression Coefficients

Functions for fitting models using various algorithms.

kernelpls.fit(): Kernel PLS (Dayal and MacGregor)
nipals.fit(): NIPALS PLS with missing values
nipalspc.fit(): NIPALS PCR with missing values
cppls.fit(): CPPLS (Indahl et al.)
oscorespls.fit(): Orthogonal scores PLSR
widekernelpls.fit(): Wide Kernel PLS (Rännar et al.)
simpls.fit(): Sijmen de Jong's SIMPLS
svdpc.fit(): Principal Component Regression
msc() predict(<msc>) makepredictcall(<msc>): Multiplicative Scatter Correction
stdize() predict(<stdized>) makepredictcall(<stdized>): Standardization of Data Matrices

Data sets included in the package.