fda.usc (version 2.1.0)

h.default: Calculation of the smoothing parameter (h) for a functional data

Description

Calculation of the smoothing parameter (h) for a functional data using nonparametric kernel estimation.

Usage

h.default(
  fdataobj,
  prob = c(0.025, 0.25),
  len = 51,
  metric = metric.lp,
  type.S = "S.NW",
  Ker = Ker.norm,
  ...
)

Value

Returns the vector of smoothing parameter or bandwidth h.

Arguments

fdataobj

fdata class object.

prob

Vector of probabilities for extracting the quantiles of the distance matrix. If length(prob)=2 a sequence between prob[1] and prob[2] of length len.

len

Vector length of smoothing parameter h to return only used when length(prob)=2.

metric

If is a function: name of the function to calculate the distance matrix between the curves, by default metric.lp. If is a matrix: distance matrix between the curves. kernel.

type.S

Type of smothing matrix S. Possible values are: Nadaraya-Watson estimator "S.NW" and K nearest neighbors estimator "S.KNN"

Ker

Kernel function. By default, Ker.norm. Useful for scaling the bandwidth values according to Kernel

...

Arguments to be passed for metric argument.

Author

Manuel Febrero-Bande, Manuel Oviedo de la Fuente

See Also

See Also as metric.lp, Kernel and S.NW.
Function used in fregre.np and fregre.np.cv function.

Examples

if (FALSE) {
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
}