slopeHeuristic: Calculate slope heuristic

In funHDDC: Univariate and Multivariate Model-Based Clustering in Group-Specific Functional Subspaces

Description

It provides slope heuristic for number of groups selection or model selection (see Schmutz et al, 2018).

Usage

  slopeHeuristic(mod)

Arguments

mod	A funHDDC object with different number of groups tested or different models.

Details

This function works for model selection. If you test one model (for example AkjBQkDk) and multiple clusters, the function returns the number of clusters selected by slope heuristic. If you test multiple models (for example all 6 models) and one or multiple values of clusters, the function returns the rank of the best model selected by slope heuristic. The rank is the first column of the output provided by funHDDC function.

Value

The best partition if the function is used for the selection of number of clusters. The index of the best model, among all estimated models if the function is used for model selection.

Examples

    ##Univariate example: multiple clusters, one model tested
    data("trigo")
    basis<- create.bspline.basis(c(0,1), nbasis=25)
    var1<-smooth.basis(argvals=seq(0,1,length.out = 100),y=t(trigo[,1:100]),fdParobj=basis)$fd
    res.uni<-funHDDC(var1,K=2:10,model="AkBkQkDk")
    slopeHeuristic(res.uni)

    
      # ##Univariate example: one cluster, 6 models tested
      # data("trigo")
      # basis<- create.bspline.basis(c(0,1), nbasis=25)
      # var1<-smooth.basis(argvals=seq(0,1,length.out = 100),y=t(trigo[,1:100]),fdParobj=basis)$fd
      # res.uni<-funHDDC(var1,K=2,model=c("AkjBkQkDk","AkjBQkDk","AkBkQkDk",
      #                                   "AkBQkDk","ABkQkDk","ABQkDk"))
      # slopeHeuristic(res.uni)
      # 
      # ##Multivariate example
      # data("triangle")
      # basis <- create.bspline.basis(c(1,21), nbasis=25)
      # var1<-smooth.basis(argvals=seq(from=1,to=21,length.out = 101),y=t(triangle[,1:101]),
      #                    fdParobj=basis)$fd
      # var2<-smooth.basis(argvals=seq(from=1,to=21,length.out = 101),y=t(triangle[,102:202]),
      #                    fdParobj=basis)$fd
      # res.multi<-funHDDC(list(var1,var2),K=2:10,model="AkjBQkDk")
      # slopeHeuristic(res.multi)
      # 
      # ##An other example on Canada dataset
      # #Clustering the "Canadian temperature" data (Ramsey & Silverman): univariate case
      # daybasis65 <- create.fourier.basis(c(0, 365), nbasis=65, period=365)
      # daytempfd <- smooth.basis(day.5, CanadianWeather$dailyAv[,,"Temperature.C"], daybasis65,
      #                           fdnames=list("Day", "Station", "Deg C"))$fd
      # 
      # res.uni<-funHDDC(daytempfd,K=2:10,model="AkjBkQkDk",threshold=0.2)
      # 
      # slopeHeuristic(res.uni)
      # 
      # #Clustering the "Canadian temperature" data (Ramsey & Silverman): multivariate case
      # daybasis65 <- create.fourier.basis(c(0, 365), nbasis=65, period=365)
      # daytempfd <- smooth.basis(day.5, CanadianWeather$dailyAv[,,"Temperature.C"], daybasis65,
      #                           fdnames=list("Day", "Station", "Deg C"))$fd
      # dayprecfd<-smooth.basis(day.5, CanadianWeather$dailyAv[,,"Precipitation.mm"], daybasis65,
      #                         fdnames=list("Day", "Station", "Mm"))$fd
      # 
      # res.multi<-funHDDC(list(daytempfd,dayprecfd),K=2:8,model="AkjBkQkDk",
      #                    init="random",threshold=0.2)
      # 
      # slopeHeuristic(res.multi)
    

funHDDC documentation built on March 17, 2021, 5:06 p.m.