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AIC for Basis Representation

Source: R/basis.R
basis.aic.Rd

Computes the Akaike Information Criterion for a basis representation. Lower AIC indicates better model (balancing fit and complexity).

Usage

basis.aic(
  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 AIC across all curves. If FALSE, compute AIC for each curve and return the mean.

Value

The AIC value (scalar).

Details

AIC is computed as: $$AIC = n \log(RSS/n) + 2 \cdot edf$$

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.