BinaryPLSR: Partial Least Squares Regression with Binary Data

In MultBiplotR: Multivariate Analysis Using Biplots in R

Partial Least Squares Regression with Binary Data

Description

Fits Partial Least Squares Regression with Binary Data

Usage

BinaryPLSR(Y, X, S = 2, tolerance = 5e-05, maxiter = 100, show = FALSE,
                   penalization = 0.1, OptimMethod = "CG", seed = 0)

Arguments

Y	The response
X	The matrix of independent variables
S	The Dimension of the solution
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
OptimMethod	Optimization methods from optim
seed	Seed. By default is 0.

Details

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

Value

Method	Description of 'comp1'
X	The predictors matrix
Y	The responses matrix
ScaledX	The scaled X matrix
tolerance	Tolerance used in the algorithm
maxiter	Maximum number of iterations used
penalization	Ridge penalization
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

XStructure	Correlations among the X variables and the PLS scores
InterceptsY	Intercepts for the Y loadings
InterceptsX	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

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.

Vicente-Gonzalez, L., & Vicente-Villardon, J. L. (2022). Partial Least Squares Regression for Binary Responses and Its Associated Biplot Representation. Mathematics, 10(15), 2580.

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.