Performs Partial Least Squares regression and returns component scores
for functional data using the NIPALS algorithm.
Usage
fdata2pls(fdataobj, y, ncomp = 2, lambda = 0, norm = TRUE)
Arguments
- fdataobj
An object of class 'fdata'.
- y
Response vector (numeric).
- ncomp
Number of PLS components to extract (default 2).
- lambda
Regularization parameter (default 0, not currently used).
- norm
Logical. If TRUE (default), normalize the scores.
Value
A list with components:
- weights
Matrix of PLS weights (m x ncomp)
- scores
Matrix of PLS scores (n x ncomp)
- loadings
Matrix of PLS loadings (m x ncomp)
- call
The function call
Examples
t <- seq(0, 1, length.out = 50)
X <- matrix(0, 20, 50)
for (i in 1:20) X[i, ] <- sin(2*pi*t) + rnorm(50, sd = 0.1)
y <- rowMeans(X) + rnorm(20, sd = 0.1)
fd <- fdata(X, argvals = t)
pls <- fdata2pls(fd, y, ncomp = 3)