Computes distribution-free prediction intervals using conformal inference.
Usage
fregre.conformal(
model,
train.data,
train.y,
test.data,
cal.fraction = 0.2,
alpha = 0.1,
seed = NULL
)
Arguments
- model
A fitted fregre.lm model.
- train.data
An fdata object (training data).
- train.y
Training response vector.
- test.data
An fdata object (test data).
- cal.fraction
Fraction of training data for calibration (default 0.2).
- alpha
Miscoverage level (default 0.1 for 90% intervals).
- seed
Random seed.
Value
A list with predictions, lower, upper, residual_quantile,
and coverage.
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)
new_fd <- fdata(matrix(rnorm(50), nrow = 5), argvals = seq(0, 1, length.out = 10))
result <- fregre.conformal(fit, fd, y, new_fd)
# }