sNPLS: Fit a sNPLS model

In David-Hervas/sNPLS: NPLS Regression with L1 Penalization

Fit a sNPLS model

Description

Fits a N-PLS regression model imposing sparsity on wj and wk matrices

Usage

sNPLS(
  XN,
  Y,
  ncomp = 2,
  threshold_j = 0.5,
  threshold_k = 0.5,
  keepJ = NULL,
  keepK = NULL,
  scale.X = TRUE,
  center.X = TRUE,
  scale.Y = TRUE,
  center.Y = TRUE,
  conver = 1e-16,
  max.iteration = 10000,
  silent = F,
  method = "sNPLS"
)

Arguments

XN	A three-way array containing the predictors.
Y	A matrix containing the response.
ncomp	Number of components in the projection
threshold_j	Threshold value on Wj. Scaled between [0, 1)
threshold_k	Threshold value on Wk. scaled between [0, 1)
keepJ	Number of variables to keep for each component, ignored if threshold_j is provided
keepK	Number of 'times' to keep for each component, ignored if threshold_k is provided
scale.X	Perform unit variance scaling on X?
center.X	Perform mean centering on X?
scale.Y	Perform unit variance scaling on Y?
center.Y	Perform mean centering on Y?
conver	Convergence criterion
max.iteration	Maximum number of iterations
silent	Show output?
method	Select between L1 penalization (sNPLS), variable selection with Selectivity Ratio (sNPLS-SR) or variable selection with VIP (sNPLS-VIP)

Value

A fitted sNPLS model

References

C. A. Andersson and R. Bro. The N-way Toolbox for MATLAB Chemometrics & Intelligent Laboratory Systems. 52 (1):1-4, 2000.

Hervas, D. Prats-Montalban, J. M., Garcia-Cañaveras, J. C., Lahoz, A., & Ferrer, A. (2019). Sparse N-way partial least squares by L1-penalization. Chemometrics and Intelligent Laboratory Systems, 185, 85-91.

Examples

X_npls<-array(rpois(7500, 10), dim=c(50, 50, 3))

Y_npls <- matrix(2+0.4*X_npls[,5,1]+0.7*X_npls[,10,1]-0.9*X_npls[,15,1]+
0.6*X_npls[,20,1]- 0.5*X_npls[,25,1]+rnorm(50), ncol=1)
#Discrete thresholding
fit <- sNPLS(X_npls, Y_npls, ncomp=3, keepJ = rep(2,3) , keepK = rep(1,3))
#Continuous thresholding
fit2 <- sNPLS(X_npls, Y_npls, ncomp=3, threshold_j=0.5, threshold_k=0.5)
#USe sNPLS-SR method
fit3 <- sNPLS(X_npls, Y_npls, ncomp=3, threshold_j=0.5, threshold_k=0.5, method="sNPLS-SR")

David-Hervas/sNPLS documentation built on Feb. 1, 2024, 6:30 a.m.