fda.usc (version 1.2.3)

classif.np: Kernel Classifier from Functional Data

Description

Fits Nonparametric Supervised Classification for Functional Data.

Usage

classif.np(group,fdataobj,h=NULL,Ker=AKer.norm,metric,
type.CV = GCV.S,type.S=S.NW,par.CV=list(trim=0),par.S=list(),...)
classif.knn(group,fdataobj,knn=NULL,metric,
type.CV = GCV.S,par.CV=list(trim=0),par.S=list(),...)
classif.kernel(group,fdataobj,h=NULL,Ker=AKer.norm,metric,
type.CV = GCV.S,par.CV=list(trim=0),par.S=list(),...)

Arguments

group

Factor of length n

fdataobj

fdata class object.

h

Vector of smoothing parameter or bandwidth.

knn

Vector of number of nearest neighbors considered.

Ker

Type of kernel used.

metric

Metric function, by default metric.lp.

type.CV

Type of cross-validation. By default generalized cross-validation GCV.S method.

type.S

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

par.CV

List of parameters for type.CV: trim, the alpha of the trimming and draw=TRUE.

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.

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.

Details

Make the group classification of a training dataset using kernel or KNN 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

Examples

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

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