classif.depth: Classifier from Functional Data

In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

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.

Author(s)

Febrero-Bande, M. and Oviedo de la Fuente, M.

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)

## End(Not run)

fda.usc documentation built on April 4, 2025, 4:35 a.m.