fda.usc (version 1.2.3)

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,
Ker = "AKer.norm", type.S ="S.NW",...)

Arguments

fdataobj

fdata class object.

prob

Range of probabilities for the quantiles of the distance matrix.

len

Vector length of smoothing parameter h to return.

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.

Ker

Type of asymmetric kernel used, by default asymmetric normal kernel.

type.S

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

...

Arguments to be passed for metric argument.

Value

Returns the vector of smoothing parameter or bandwidth h.

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)
# h1;h2;h3;h4