Fits Partial Least Squares Regression with several Binary Responses
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)Description of 'comp1'
The predictors matrix
The responses matrix
Initial Transformation of the X matrix
The scaled X matrix
Tolerance used in the algorithm
Maximum number of iterations used
Ridge penalization
Is the constant included in the model?
Scores of the X matrix, used later for the biplot
Loadings of the X matrix
Scores of the Y matrix
Loadings of the Y matrix
Regression coefficients
Correlations among the X variables and the PLS scores
Intercepts for the Y loadings
Linear terms for each response
Expected probabilities for the responses
Binary predictions of the responses
Global percent of correct predictions
Percent of correct predictions for each column
Column with the maximum probability. Useful when the responses are the indicators of a multinomial variable
The response
The matrix of independent variables
The Dimension of the solution
Initial transform for the X matrix
Grouping variable when the inial transformation is standardization within groups.
Tolerance for convergence of the algorithm
Maximum Number of iterations
Show the steps of the algorithm
Penalization for the Ridge Logistic Regression
Should a constant be included in the model?
Optimization methods from optim
The responses are the indicators of a multinomial variable?
José Luis Vicente Villardon
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.
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.
# \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)
# }
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