PLSR: Partial Least Squares Regression

In villardon/MultBiplotR: Multivariate Analysis Using Biplots in R

Description

Partial Least Squares Regression for numerical variables.

Usage

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.

See Also

Biplot.PLSR

Examples

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

villardon/MultBiplotR documentation built on June 5, 2021, 8:55 a.m.