PLSc {powerPLS}

PLS

Description

Performs Partial Least Squares two class

Usage

PLSc(X, Y, A, scaling = "auto-scaling", post.transformation = TRUE,
eps = 0.01, Y.prob = FALSE, transformation = "ilr")

Arguments

X

data matrix where columns represent the p variables and rows the n observations.

Y

data matrix where columns represent the two classes and rows the n observations.

A

number of score components

scaling

type of scaling, one of c("auto-scaling", "pareto-scaling", "mean-centering"). Default @auto-scaling

post.transformation

Boolean value. @TRUE if you want to apply post transformation. Default @TRUE

eps

Default 0.01. eps is used when Y.prob = FALSE to transform Y in a probability vector

Y.prob

Boolean value. Default @FALSE. IF @TRUE Y is a probability vector

transformation

transformation used to map Y in probability data vector. The options are @ilr and @clr. Default @ilr.

Value

Returns a list with the following objects: - W: matrix of weights - X_loading: matrix of X loading - Y_loading: matrix of Y loading - X: matrix of X data - Y: matrix of Y data - T_score: matrix of scores - Y_fitted: fitted Y matrix - B: Matrix regression coefficients - M: number of orthogonal components if post transformation is applied.

Author(s)

Angela Andreella

References

Stocchero, M., De Nardi, M., & Scarpa, B. (2021). PLS for classification. Chemometrics and Intelligent Laboratory Systems, 216, 104374.

Examples

datas <- simulatePilotData(nvar = 30, clus.size = c(5,5),m = 6,nvar_rel = 5,A = 2)
out <- PLSc(X = datas$X, Y = datas$Y, A = 3)