Compute Akaike's Information Criterion.
The calling sequence for aic matches those for the
locfit or locfit.raw functions.
The fit is not returned; instead, the returned object contains
Akaike's information criterion for the fit.
The definition of AIC used here is -2*log-likelihood + pen*(fitted d.f.). For quasi-likelihood, and local regression, this assumes the scale parameter is one. Other scale parameters can effectively be used by changing the penalty.
The AIC score is exact (up to numerical roundoff) if the
ev="data" argument is provided. Otherwise, the residual
sum-of-squares and degrees of freedom are computed using locfit's
standard interpolation based approximations.
aic(x, ..., pen=2)
x |
model formula |
... |
other arguments to locfit |
pen |
penalty for the degrees of freedom term |
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