PLSc {powerPLS} | R Documentation |
PLS classification
Description
Performs Partial Least Squares classification
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 |
Y |
Data matrix where columns represent the two classes and
rows the |
A |
Number of score components |
scaling |
Type of scaling, one of
|
post.transformation |
Boolean value. |
eps |
Default 0.01. |
Y.prob |
Boolean value. Default |
transformation |
Transformation used to map |
Value
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 (predictor variables)- Y
Matrix of
Y
data (dependent variable)- T_score
Matrix of scores
- Y_fitted
Fitted
Y
matrix- B
Matrix regression coefficients
- M
Number of orthogonal components if
post.transformation=TRUE
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)