fda.usc (version 2.0.1)

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.

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 {
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)
# }
# NOT RUN {
# }