mcuve_pls: Uninformative variable elimination in PLS (UVE-PLS)
Description
Artificial noise variables are added to the predictor set before the PLSR
model is fitted. All the original variables having lower "importance" than the artificial
noise variables are eliminated before the procedure is repeated until a stop criterion is
reached.
Usage
mcuve_pls(y, X, ncomp = 10, N = 3, ratio = 0.75, MCUVE.threshold = NA)
Value
Returns a vector of variable numbers corresponding to the model
having lowest prediction error.
Arguments
y
vector of response values (numeric or factor).
X
numeric predictor matrix.
ncomp
integer number of components (default = 10).
N
number of samples Mone Carlo simulations (default = 3).
ratio
the proportion of the samples to use for calibration (default = 0.75).
MCUVE.threshold
thresholding separate signal from noise (default = NA creates
automatic threshold from data).
Author
Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.
References
V. Centner, D. Massart, O. de Noord, S. de Jong, B. Vandeginste, C. Sterna,
Elimination of uninformative variables for multivariate calibration, Analytical Chemistry
68 (1996) 3851-3858.