gcv matches those for the
locfit or locfit.raw functions.
The fit is not returned; instead, the returned object contains
Wahba's generalized cross-validation score for the fit. The GCV 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.
For likelihood models, GCV is computed uses the deviance in place of the residual sum of squares. This produces useful results but I do not know of any theory validating this extension.
gcv(x, ...)locfit).locfit.locfit,
locfit.raw,
gcvplot