MultBiplotR (version 23.11.0)

Biplot.BinaryPLSR: 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)

Value

An object of class Binary.Logistic.Biplot

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

Author

Jose Luis Vicente Villardon

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.

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)
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)
# }