fda.usc (version 1.5.0)

classif.depth: Classifier from Functional Data

Description

Classification of functional data using maximum depth.

Usage

classif.depth(group,fdataobj,newfdataobj,depth="RP",
par.depth=list(),CV="none")

Arguments

group

Factor of length n

fdataobj

fdata, matrix or data.frame class object of train data.

newfdataobj

fdata, matrix or data.frame class object of test data.

depth

Type of depth function from functional data:

• FM: Fraiman and Muniz depth.

• mode: modal depth.

• RT: random Tukey depth.

• RP: random project depth.

• RPD: double random project depth.

par.depth

List of parameters for depth.

CV

=``none'' group.est=group.pred, =TRUE group.est is estimated by cross-validation, =FALSE group.est is estimated.

Value

group.est

Vector of classes of train sample data.

group.pred

Vector of classes of test sample data.

prob.classification

Probability of correct classification by group.

max.prob

Highest probability of correct classification.

fdataobj

fdata class object.

group

Factor of length n.

%\item{prob.group}{ Matrix of predicted class probabilities. For each functional point shows the probability of each possible group membership.}

References

Cuevas, A., Febrero-Bande, M. and Fraiman, R. (2007). Robust estimation and classification for functional data via projection-based depth notions. Computational Statistics 22, 3, 481-496.

Examples

# NOT RUN {
 
# }
# NOT RUN {
data(phoneme)
mlearn<-phoneme[["learn"]]
mtest<-phoneme[["test"]]
glearn<-phoneme[["classlearn"]]
gtest<-phoneme[["classtest"]]

a1<-classif.depth(glearn,mlearn,depth="RP")
table(a1$group.est,glearn)
a2<-classif.depth(glearn,mlearn,depth="RP",CV=TRUE)
a3<-classif.depth(glearn,mlearn,depth="RP",CV=FALSE)
a4<-classif.depth(glearn,mlearn,mtest,"RP")
a5<-classif.depth(glearn,mlearn,mtest,"RP",CV=TRUE)     
table(a5$group.est,glearn)
a6<-classif.depth(glearn,mlearn,mtest,"RP",CV=FALSE)
table(a6$group.est,glearn)
# }