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

In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

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,
  ...
)

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.

Value

Returns the vector of smoothing parameter or bandwidth h.

Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente [e-mail]

See Also

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

Examples

## 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)

fda.usc documentation built on Oct. 17, 2022, 9:06 a.m.