Measures importance of each FPC by permuting scores and measuring
the increase in prediction error.
Usage
fregre.importance(model, data, y, n.perm = 100, seed = NULL)
Arguments
- model
A fitted fregre.lm or fregre.logistic model.
- data
An fdata object (the training data).
- y
Response vector.
- n.perm
Number of permutations (default 100).
- seed
Random seed.
Value
A list with importance, baseline_metric, and permuted_metric.
Examples
# \donttest{
fd <- fdata(matrix(rnorm(500), nrow = 50), argvals = seq(0, 1, length.out = 10))
y <- rnorm(50)
fit <- fregre.lm(fd, y, ncomp = 3)
result <- fregre.importance(fit, fd, y)
# }