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Classifier from Functional Data

Source: R/classif.depth.R
classif.depth.Rd

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.

Author

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

Examples

if (FALSE) { # \dontrun{
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)
} # }