PLSR: Partial Least Squares Regression

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/PLSR.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.

See Also

Biplot.PLSR

Examples

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

MultBiplotR documentation built on April 6, 2021, 9:08 a.m.