MultBiplotR (version 23.11.0)

BinaryPLSR: 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)

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

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.

Author

José Luis Vicente Villardon

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.

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

# \donttest{
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)
# }