PLSRBin: Partial Least Squares Regression with several Binary...

In MultBiplotR: Multivariate Analysis Using Biplots in R

Partial Least Squares Regression with several Binary Responses

Description

Fits Partial Least Squares Regression with several Binary Responses

Usage

PLSRBin(Y, X, S = 2, InitTransform = 5, grouping = NULL, 
tolerance = 5e-05, maxiter = 100, show = FALSE, penalization = 0.1, 
cte = TRUE, OptimMethod = "CG", Multiple = FALSE)

Arguments

Y	The response
X	The matrix of independent variables
S	The Dimension of the solution
InitTransform	Initial transform for the X matrix
grouping	Grouping variable when the inial transformation is standardization within groups.
tolerance	Tolerance for convergence of the algorithm
maxiter	Maximum Number of iterations
show	Show the steps of the algorithm
penalization	Penalization for the Ridge Logistic Regression
cte	Should a constant be included in the model?
OptimMethod	Optimization methods from optim
Multiple	The responses are the indicators of a multinomial variable?

Details

The function fits the PLSR method for the case when there is a set binary dependent variables, using logistic rather than linear fits to take into account the nature of responses. We term the method PLS-BLR (Partial Least Squares Binary Logistic Regression). This can be considered as a generalization of the NIPALS algorithm when the responses are all binary.

Value

Method	Description of 'comp1'
X	The predictors matrix
Y	The responses matrix
Initial_Transformation	Initial Transformation of the X matrix
ScaledX	The scaled X matrix
tolerance	Tolerance used in the algorithm
maxiter	Maximum number of iterations used
penalization	Ridge penalization
IncludeConst	Is the constant included in the model?
XScores	Scores of the X matrix, used later for the biplot
XLoadings	Loadings of the X matrix
YScores	Scores of the Y matrix
YLoadings	Loadings of the Y matrix
Coefficients	Regression coefficients
XStructure	Correlations among the X variables and the PLS scores
Intercepts	Intercepts for the Y loadings
LinTerm	Linear terms for each response
Expected	Expected probabilities for the responses
Predictions	Binary predictions of the responses
PercentCorrect	Global percent of correct predictions
PercentCorrectCols	Percent of correct predictions for each column
Maxima	Column with the maximum probability. Useful when the responses are the indicators of a multinomial variable

Author(s)

José Luis Vicente Villardon

References

Ugarte Fajardo, J., Bayona Andrade, O., Criollo Bonilla, R., Cevallos‐Cevallos, J., Mariduena‐Zavala, M., Ochoa Donoso, D., & Vicente Villardon, J. L. (2020). Early detection of black Sigatoka in banana leaves using hyperspectral images. Applications in plant sciences, 8(8), e11383.

Examples

X=as.matrix(wine[,4:21])
Y=cbind(Factor2Binary(wine[,1])[,1], Factor2Binary(wine[,2])[,1])
rownames(Y)=wine[,3]
colnames(Y)=c("Year", "Origin")
pls=PLSRBin(Y,X, penalization=0.1, show=TRUE, S=2)

MultBiplotR documentation built on Nov. 21, 2023, 5:08 p.m.