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(),
...
)
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
.
Factor of length n
fdata
class object.
Vector of smoothing parameter or bandwidth.
Type of kernel used.
Metric function, by default metric.lp
.
weights.
Type of smothing matrix S
. By default S
is
calculated by Nadaraya-Watson kernel estimator (S.NW
).
List of parameters for type.S
: w
, the weights.
Arguments to be passed for metric.lp
o other
metric function and Kernel
function.
Vector of number of nearest neighbors considered.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es
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
if (FALSE) {
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)
}
Run the code above in your browser using DataLab