Conformal Prediction for Functional Linear Model

Constructs prediction intervals using split conformal inference for the FPC-based functional linear model.

Usage

conformal.fregre.lm(
  fdataobj,
  y,
  newdata,
  scalar.train = NULL,
  scalar.test = NULL,
  ncomp = 3,
  cal.fraction = 0.25,
  alpha = 0.1,
  seed = NULL
)

Arguments

fdataobj

An object of class 'fdata' (training data).

y

Response vector (training).

newdata

An object of class 'fdata' (test data).

scalar.train

Optional scalar covariates for training.

scalar.test

Optional scalar covariates for test.

ncomp

Number of FPC components (default 3).

cal.fraction

Fraction of data for calibration (default 0.25).

alpha

Miscoverage level (default 0.1 for 90 percent intervals).

seed

Random seed.

Value

A list with components:

predictions

Point predictions for test data

lower

Lower bounds of prediction intervals

upper

Upper bounds of prediction intervals

residual.quantile

Calibration residual quantile

coverage

Empirical coverage on calibration set

Examples

# \donttest{
fd <- fdata(matrix(rnorm(500), 50, 10), argvals = seq(0, 1, length.out = 10))
y <- rnorm(50)
cp <- conformal.fregre.lm(fd[1:40, ], y[1:40], fd[41:50, ], ncomp = 3)
# }