Calculation of the smoothing parameter (h) for a functional data
Calculation of the smoothing parameter (h) for a functional data using nonparametric kernel estimation.
h.default( fdataobj, prob = c(0.025, 0.25), len = 51, metric = metric.lp, type.S = "S.NW", ... )
fdataobj |
|
prob |
Range of probabilities for the quantiles of the distance matrix. |
len |
Vector length of smoothing parameter |
metric |
If is a function: name of the function to calculate the
distance matrix between the curves, by default |
type.S |
Type of smothing matrix |
... |
Arguments to be passed for metric argument. |
Returns the vector of smoothing parameter or bandwidth h.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es
See Also as metric.lp, Kernel and
S.NW.
Function used in fregre.np and
fregre.np.cv function.
## Not run: data(aemet) h1<-h.default(aemet$temp,prob=c(0.025, 0.25),len=2) mdist<-metric.lp(aemet$temp) h2<-h.default(aemet$temp,len=2,metric=mdist) h3<-h.default(aemet$temp,len=2,metric=semimetric.pca,q=2) h4<-h.default(aemet$temp,len=2,metric=semimetric.pca,q=4) h5<-h.default(aemet$temp,prob=c(.2),type.S="S.KNN") h1;h2;h3;h4;h5 ## End(Not run)
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