# h.default

0th

Percentile

##### Calculation of the smoothing parameter (h) for a functional data

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

Keywords
nonparametric
##### Usage
h.default(
fdataobj,
prob = c(0.025, 0.25),
len = 51,
metric = metric.lp,
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. 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 as metric.lp, Kernel and S.NW. Function used in fregre.np and fregre.np.cv function.

• h.default
##### 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
# }

Documentation reproduced from package fda.usc, version 2.0.1, License: GPL-2

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