Biplot.BinaryPLSR {MultBiplotR}

Biplot for a PLSR model with binary data

Description

Builds a Biplot for a PLSR model with binary data

Usage

Biplot.BinaryPLSR(plsr, BinBiplotType=1)

Arguments

plsr

A BinaryPLSR object

BinBiplotType

The type of biplot:

1:The biplot resulting from the fit, for the binary data.

2: The biplot for the coefficients

Details

Builds a Biplot for a PLSR model with binary data. The result is a biplot for the matrix with the binary predictors (X) adding the binary responses as suplementary variables. There are two possible types, 1 for the biplot directly obtained in the fit (the default) and 2 for the biplot obtaines after refitting the binary variables using Ridge Logistic Regression.

Value

An object of class Binary.Logistic.Biplot

Author(s)

Jose 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)
plsbip=Biplot.PLSRBIN(pls, BinBiplotType=1)
plsbip=AddCluster2Biplot(plsbip, ClusterType = "us", 
       Groups = wine$Group)
plot(plsbip, margin=0.05, mode="s", PlotClus = TRUE, 
    ModeSupBinVars = "s", ShowAxis = FALSE, 
    ColorSupBinVars = "blue",     CexInd=0.5, 
    ClustCenters = TRUE, LabelInd = FALSE, ShowBox = TRUE)