Package index • sgPLS

All functions

data.create() data.cl.create(): Dataset simulation

gPLS(): Group Partial Least Squares (gPLS)

gPLSda(): Group Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)

msep.PLS(): PLS function performance assessment using \(MSEP\) indicator

per.variance(): Percentage of variance of the \(Y\) matrix explained by the score-vectors obtained by PLS approaches

perf(<PLS>) perf(<sPLS>) perf(<gPLS>) perf(<sgPLS>) perf(<PLSda>) perf(<sPLSda>) perf(<gPLSda>) perf(<sgPLSda>): Compute evaluation criteria for PLS, sPLS, PLS-DA and sPLS-DA

plotcim(): Plots a cluster image mapping of correlations between outcomes and all predictors

PLS(): Partial Least Squares (PLS)

PLSda(): Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)

predict(<PLS>) predict(<sPLS>) predict(<gPLS>) predict(<sgPLS>) predict(<PLSda>) predict(<sPLSda>) predict(<gPLSda>) predict(<sgPLSda>): Predict Method for PLS, sPLS, gPLS, sgPLS, sPLDda, gPLSda, sgPLSda

q2.PLS(): PLS function performance assessment using Q2 indicator.

select.spls(): Output of selected variables from a sPLS model

normv soft.thresholding soft.thresholding.group soft.thresholding.sparse.group lambda.quadra step1.spls.sparsity step1.sparse.group.spls.sparsity step1.group.spls.sparsity step2.spls: Internal Functions

sgPLS-package: Group and Sparse Group Partial Least Square Model

sgPLS(): Sparse Group Partial Least Squares (sgPLS)

sgPLSda(): Sparse Group Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)

simuData: Simulated Data for group PLS-DA model

sPLS(): Sparse Partial Least Squares (sPLS)

sPLSda(): Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)

tuning.gPLS.X(): Choice of the tuning parameter (number of groups) related to predictor matrix for gPLS model (regression mode)

tuning.sgPLS.X(): Choice of the tuning parameters (number of groups and mixing parameter) related to predictor matrix for sgPLS model (regression mode)

tuning.sPLS.X(): Choice of the tuning parameter (number of variables) related to predictor matrix for sPLS model (regression mode)