# h.default: Calculation of the smoothing parameter (h) for a functional... In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

 h.default R Documentation

## 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 manuel.oviedo@udc.es

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.