classif.np: Kernel Classifier from Functional Data

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Description

Fits Nonparametric Supervised Classification for Functional Data.

Usage

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classif.np(
  group,
  fdataobj,
  h = NULL,
  Ker = AKer.norm,
  metric,
  weights = "equal",
  type.S = S.NW,
  par.S = list(),
  ...
)

classif.knn(
  group,
  fdataobj,
  knn = NULL,
  metric,
  weights = "equal",
  par.S = list(),
  ...
)

classif.kernel(
  group,
  fdataobj,
  h = NULL,
  Ker = AKer.norm,
  metric,
  weights = "equal",
  par.S = list(),
  ...
)

Arguments

group

Factor of length n

fdataobj

fdata class object.

h

Vector of smoothing parameter or bandwidth.

Ker

Type of kernel used.

metric

Metric function, by default metric.lp.

weights

weights.

type.S

Type of smothing matrix S. By default S is calculated by Nadaraya-Watson kernel estimator (S.NW).

par.S

List of parameters for type.S: w, the weights.

...

Arguments to be passed for metric.lp o other metric function and Kernel function.

knn

Vector of number of nearest neighbors considered.

Details

Make the group classification of a training dataset using kernel or KNN estimation: Kernel.
Different types of metric funtions can be used.

Value

Note

If fdataobj is a data.frame the function considers the case of multivariate covariates.
metric.dist function is used to compute the distances between the rows of a data matrix (as dist function.

Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es

References

Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis. Springer Series in Statistics, New York.

Ferraty, F. and Vieu, P. (2006). NPFDA in practice. Free access on line at http://www.lsp.ups-tlse.fr/staph/npfda/

See Also

See Also as predict.classif

Examples

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## Not run: 
data(phoneme)
mlearn<-phoneme[["learn"]]
glearn<-phoneme[["classlearn"]]

h=9:19
out=classif.np(glearn,mlearn,h=h)
summary(out)
# round(out$prob.group,4)

## End(Not run)

fda.usc documentation built on Feb. 18, 2020, 1:07 a.m.