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BIC for Basis Representation

Source: R/basis.R
basis.bic.Rd

Computes the Bayesian Information Criterion for a basis representation. BIC penalizes complexity more strongly than AIC for larger samples.

Usage

basis.bic(
  fdataobj,
  nbasis,
  type = c("bspline", "fourier"),
  lambda = 0,
  pooled = TRUE
)

Arguments

fdataobj

An fdata object.

nbasis

Number of basis functions.

type

Basis type: "bspline" (default) or "fourier".

lambda

Smoothing/penalty parameter (default 0).

pooled

Logical. If TRUE (default), compute a single BIC across all curves. If FALSE, compute BIC for each curve and return the mean.

Value

The BIC value (scalar).

Details

BIC is computed as: $$BIC = n \log(RSS/n) + \log(n) \cdot edf$$

When pooled = TRUE, the criterion uses total observations and total effective degrees of freedom (n_curves * edf). When pooled = FALSE, the criterion is computed for each curve separately and the mean is returned.