PLSR: Partial Least Squares Regression In MultBiplotR: Multivariate Analysis Using Biplots in R

Description

Partial Least Squares Regression for numerical variables.

Usage

 ```1 2 3``` ```PLSR(Y, X, S = 2, InitTransform = 5, grouping = NULL, centerY = TRUE, scaleY = TRUE, tolerance = 5e-06, maxiter = 100, show = FALSE, Validation = NULL, nB = 500) ```

Arguments

 `Y` Matrix of Dependent Variables `X` Matrix of Independent Variables `S` Dimension of the solution `InitTransform` Initial transformation of the independent variables. `grouping` Fator when the init transformation is the standardization with the within groups deviation. `centerY` Should the dependent variables be centered? `scaleY` Should the dependent variables be standadized? `tolerance` Tolerance for the algorithm `maxiter` Maximum number of iterations `show` Show the progress of the algorithm? `Validation` Validation (None, Cross, Bootstrap) `nB` number of samples for the bottstrap validation

Details

Partial Least Squares Regression for numerical variables.

Value

An object of class plsr with fiends

 `Method` PLSR `X` The X matrix `Y` The Y matrix `centerY` Is the Y matrix centered `scaleY` Is the Y matrix scaled `Initial_Transformation` Initial transformation of the Y matrix `ScaledX` Transformed X matrix `ScaledY` Transformed Y matrix `Intercept` Intercept of the model `XScores` Scores for the individals from the X matrix `XWeights` Weigths for the X set `XLoadings` Loadings for the X set `YScores` Scores for the individals from the Y matrix `YWeights` Weigths for the Y set `YLoadings` Loadings for the Y set `RegParameters` Final Regression Parameters `ExpectedY` Expected values of Y `R2` R-squared `XStructure` Relation of the X variables with its structure `YStructure` Relation of the Y variables with its structure `YXStructure` Relation of the Y variables with the X components

Author(s)

Jose Luis Vicente Villardon

References

H. Abdi, Partial least squares regression and projection on latent structure regression (PLS regression), WIREs Comput. Stat. 2 (2010), pp. 97-106.

`Biplot.PLSR`
 ```1 2 3``` ```X=as.matrix(wine[,4:21]) y=as.numeric(wine[,2])-1 mifit=PLSR(y,X, Validation="None") ```