Fits Nonparametric Supervised Classification for Functional Data.
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(), ... )
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(), ... )
Factor of length n
fdata class object.
fdata
Vector of smoothing parameter or bandwidth.
Type of kernel used.
Metric function, by default metric.lp.
metric.lp
weights.
Type of smothing matrix S. By default S is calculated by Nadaraya-Watson kernel estimator (S.NW).
S
S.NW
List of parameters for type.S: w, the weights.
type.S
w
Arguments to be passed for metric.lp o other metric function and Kernel function.
Kernel
Vector of number of nearest neighbors considered.
fdataobj fdata class object.
group Factor of length n.
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.
hh.opt
prob.classification Probability of correct classification by group.
misclassification Vector of probability of misclassification by number of neighbors knn.
knn
h Vector of smoothing parameter or bandwidht.
C A call of function classif.kernel.
classif.kernel
Make the group classification of a training dataset using kernel or KNN estimation: Kernel. Different types of metric funtions can be used.
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 as predict.classif
predict.classif
# 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) # } # NOT RUN { # }
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