fda.usc (version 0.9.4)

classif.kernel.fd: Kernel Classifier from Functional Data

Description

Fits Nonparametric Supervised Classification for Functional Data.

Usage

classif.kernel.fd(fdataobj, group, h = NULL, 
metric = metric.lp, ...)

Arguments

fdataobj

fdata class object.

group

Factor of length n.

h

Vector of smoothing parameter or bandwidth.

metric

Metric function, by default metric.lp.

...

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

Value

• fdataobj
  fdata class object.
• group
  Factor of length n.
• group.est
  Estimated vector groups
• prob.group
  Matrix of predicted class probabilities. For each functional point shows the probability of each possible group membership.
• max.prob
  Highest probability of correct classification.
• h.opt
  Optimal smoothing parameter or bandwidht estimated.
• D
  Matrix of distances of the optimal quantile distance hh.opt.
• prob.classification
  Probability of correct classification by group.
• misclassification
  Vector of probability of misclassification by number of neighbors knn.
• h
  Vector of smoothing parameter or bandwidht.
• C
  A call of function classif.kernel.fd.

Details

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

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.fd

Examples

data(phoneme)
mlearn<-phoneme[["learn"]]
glearn<-phoneme[["classlearn"]]

h=seq(7,19,len=10)
out=classif.kernel.fd(mlearn,glearn,h=h)
summary.classif.fd(out)
#round(out$prob.group,4)