Cross-Validated Functional Classification

Evaluates classification error rate using k-fold cross-validation.

Usage

fclassif.cv(
  fdataobj,
  y,
  method = "lda",
  covariates = NULL,
  ncomp = 3,
  nfold = 10,
  seed = NULL,
  ...
)

Arguments

fdataobj

An object of class 'fdata'.

y

Integer vector of class labels.

method

Classification method (default "lda").

covariates

Optional scalar covariates matrix.

ncomp

Number of FPC components (default 3).

nfold

Number of CV folds (default 10).

seed

Random seed for fold assignment.

...

Additional arguments passed to the classifier. For SVM:

kernel: SVM kernel: "radial" (default), "linear", "polynomial", "sigmoid".
cost: SVM cost parameter (default 1).
gamma: SVM kernel parameter (default 1/ncomp).

Value

An object of class 'fclassif.cv' with components:

error.rate: Mean error rate across folds
fold.errors: Per-fold error rates
best.ncomp: Best ncomp if tuned

Examples

# \donttest{
fd <- fdata(matrix(rnorm(500), 50, 10), argvals = seq(0, 1, length.out = 10))
y <- rep(1:2, each = 25)
cv <- fclassif.cv(fd, y, method = "lda", ncomp = 3, nfold = 5)
cv$error.rate
#> [1] 0.48
# }