# GCCV.S

0th

Percentile

##### The generalized correlated cross-validation (GCCV) score.

The generalized correlated cross-validation (GCV) score.

Keywords
utilities
##### Usage
GCCV.S(
y,
S,
criteria = "GCCV1",
W = NULL,
trim = 0,
draw = FALSE,
metric = metric.lp,
...
)
##### Arguments
y

Response vectorith length n or Matrix of set cases with dimension (n x m), where n is the number of curves and m are the points observed in each curve.

S

Smoothing matrix, see S.NW, S.LLR or $S.KNN$.

criteria

The penalizing function. By default "Rice" criteria. "GCCV1","GCCV2","GCCV3","GCV") Possible values are "GCCV1", "GCCV2", "GCCV3", "GCV".

W

Matrix of weights.

trim

The alpha of the trimming.

draw

=TRUE, draw the curves, the sample median and trimmed mean.

metric

Metric function, by default metric.lp.

Further arguments passed to or from other methods.

##### Details

$$GCCV=\frac{\sum_{i=1}^n {y_{i}-\hat{y}_{i,b}}^2}{1-\frac{tr(C)}{n}^2}$$ $cor(\epsilon_i,\epsilon_j ) =\sigma$

where $S$ is the smoothing matrix $S$ and: A.-If $C=2S\Sigma - S\Sigma S$ B.-If $C=S\Sigma$ C.-If $C=S\Sigma S'$ with $\Sigma$ is the n x n covariance matrix with $cor(\epsilon_i,\epsilon_j ) =\sigma$

##### Value

Returns GCCV score calculated for input parameters.

##### Note

Provided that $C = I$ and the smoother matrix S is symmetric and idempotent, as is the case for many linear fitting techniques, the trace term reduces to $n - tr[S]$, which is proportional to the familiar denominator in GCV.

##### References

Carmack, P. S., Spence, J. S., and Schucany, W. R. (2012). Generalised correlated cross-validation. Journal of Nonparametric Statistics, 24(2):269--282.

Oviedo de la Fuente, M., Febrero-Bande, M., Pilar Munoz, and Dominguez, A. Predicting seasonal influenza transmission using Functional Regression Models with Temporal Dependence. arXiv:1610.08718. https://arxiv.org/abs/1610.08718

See Also as optim.np. Alternative method (independent case): GCV.S

• GCCV.S
##### Examples
# NOT RUN {
data(tecator)
x=tecator$absorp.fdata x.d2<-fdata.deriv(x,nderiv=) tt<-x[["argvals"]] dataf=as.data.frame(tecator$y)
y=tecator$y$Fat
# plot the response
plot(ts(tecator$y$Fat))

nbasis.x=11;nbasis.b=7
basis1=create.bspline.basis(rangeval=range(tt),nbasis=nbasis.x)
basis2=create.bspline.basis(rangeval=range(tt),nbasis=nbasis.b)
basis.x=list("x.d2"=basis1)
basis.b=list("x.d2"=basis2)
ldata=list("df"=dataf,"x.d2"=x.d2)
# No correlation
res.gls=fregre.gls(Fat~x.d2,data=ldata,
basis.x=basis.x,basis.b=basis.b)
# AR1 correlation
res.gls=fregre.gls(Fat~x.d2,data=ldata, correlation=corAR1(),
basis.x=basis.x,basis.b=basis.b)
GCCV.S(y,res.gls$H,"GCCV1",W=res.gls$W)
res.gls\$gcv
# }

Documentation reproduced from package fda.usc, version 2.0.1, License: GPL-2

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