Functional Logistic Regression

Binary logistic regression with functional and optional scalar predictors using FPC projection and IRLS.

Usage

functional.logistic(
  fdataobj,
  y,
  scalar.covariates = NULL,
  ncomp = 3,
  max.iter = 100,
  tol = 1e-06
)

Arguments

fdataobj

An object of class 'fdata'.

y

Binary response vector (0/1).

scalar.covariates

Optional matrix of scalar covariates.

ncomp

Number of FPC components (default 3).

max.iter

Maximum IRLS iterations (default 100).

tol

Convergence tolerance (default 1e-6).

Value

A fitted object of class 'fregre.logistic' with components:

probabilities

Predicted probabilities P(Y=1)

predicted.classes

Predicted class labels (0 or 1)

accuracy

Classification accuracy on training data

log.likelihood

Log-likelihood at convergence

Examples

# \donttest{
set.seed(42)
t_grid <- seq(0, 1, length.out = 30)
X <- matrix(0, 40, 30)
for (i in 1:40) X[i, ] <- sin(2*pi*t_grid) * (2*(i > 20) - 1) + rnorm(30, sd = 0.2)
fd <- fdata(X, argvals = t_grid)
y <- as.numeric(1:40 > 20)
result <- functional.logistic(fd, y, ncomp = 3)
result$accuracy
#> [1] 1
# }