Generic Conformal Classification

Split-conformal prediction sets using a pre-fitted functional.logistic model. Only supports binary classification (2 classes).

Usage

conformal.generic.classification(
  model,
  fdataobj,
  y,
  newdata,
  scalar.train = NULL,
  scalar.test = NULL,
  score.type = c("lac", "aps"),
  calibration.indices = NULL,
  cal.fraction = 0.25,
  alpha = 0.1,
  seed = NULL
)

Arguments

model

A fitted model object from functional.logistic.

fdataobj

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

y

Binary response (0/1). Only binary (2-class) classification is supported; for multiclass use conformal.classif.

newdata

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

scalar.train

Optional scalar covariates for training.

scalar.test

Optional scalar covariates for test.

score.type

Nonconformity score: "lac" or "aps" (default "lac").

calibration.indices

Optional integer vector of 1-based indices into the training data to use as the calibration set. When provided, these observations should have been held out during model fitting so that calibration scores are out-of-sample, restoring the coverage guarantee.

cal.fraction

Calibration fraction (default 0.25). Ignored when calibration.indices is provided.

alpha

Miscoverage level (default 0.1).

seed

Random seed.

Value

Same as conformal.classif.

Warning

The model was trained on ALL data including the calibration subset, so calibration scores are in-sample and the coverage guarantee is broken. Use conformal.logistic or cv.conformal.classification for valid coverage. For multiclass problems, use conformal.classif.

Examples

# \donttest{
fd <- fdata(matrix(rnorm(500), 50, 10), argvals = seq(0, 1, length.out = 10))
y <- rbinom(50, 1, 0.5)
model <- functional.logistic(fd, y)
cp <- conformal.generic.classification(model, fd, y, fd[1:10, ])
#> Warning: conformal.generic.classification uses the pre-fitted model without refitting. Calibration scores are in-sample, so coverage guarantee is broken. Supply calibration.indices (held-out indices) for valid coverage, or use conformal.logistic() / cv.conformal.classification() instead.
# }