Conformal Prediction for Logistic Regression

Usage

conformal.logistic(
  fdataobj,
  y,
  newdata,
  scalar.train = NULL,
  scalar.test = NULL,
  ncomp = 3,
  max.iter = 100,
  tol = 1e-06,
  score.type = c("lac", "aps"),
  cal.fraction = 0.25,
  alpha = 0.1,
  seed = NULL
)

Arguments

fdataobj: An object of class 'fdata' (training data).
y: Binary response (0/1).
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).
max.iter: Maximum iterations (default 100).
tol: Convergence tolerance (default 1e-6).
score.type: Nonconformity score: "lac" or "aps".
cal.fraction: Calibration fraction (default 0.25).
alpha: Miscoverage level (default 0.1).
seed: Random seed.

Value

Same as conformal.classif.

Examples

# \donttest{
fd <- fdata(matrix(rnorm(500), 50, 10), argvals = seq(0, 1, length.out = 10))
y <- rbinom(50, 1, 0.5)
cp <- conformal.logistic(fd[1:40, ], y[1:40], fd[41:50, ])
# }