funHDDC (version 2.0)

predict.funHDDC: Predict new results from a funHDDC model

Description

Prediction for a new functional dataset using a funHDDC model previously trained.

Usage

# S3 method for funHDDC
predict(object,newdata,...)

Arguments

object

An object produced by funHDDC function

newdata

In the univariate case: a functional data object produced by the fda package, in the multivariate case: a list of functional data objects, for which you want to predict the group membership from the model.

...

Arguments to be passed to methods, such as graphical parameters.

Value

class

The clustering partition.

t

The probability of each individual to belong to each cluster.

L

The loglikelihood.

References

- C. Bouveyron and J. Jacques, Model-based Clustering of Time Series in Group-specific Functional Subspaces, Advances in Data Analysis and Classification, vol. 5 (4), pp. 281-300, 2011. - A. Schmutz, C. Bouveyron, J. Jacques, L. Cheze and P. Martin, Clustering multivariate functional data in group-specic functional subspaces, Preprint HAL 01652467, Universit<U+00E9> C<U+00F4>te d'Azur, 2017.

Examples

# NOT RUN {
##Univariate example
    library(fda)
    data("trigo")
    basis<- create.bspline.basis(c(0,1), nbasis=25)
    z<-sample(1:100,0.8*100,replace=FALSE)
    var1_train<-smooth.basis(argvals=seq(0,1,length.out = 100),y=t(trigo[z,1:100]),
                             fdParobj=basis)$fd
    var1_test<-smooth.basis(argvals=seq(0,1,length.out = 100),y=t(trigo[-z,1:100]),
                            fdParobj=basis)$fd

    model<-funHDDC(var1_train,K=2)
    pred<-predict(model,var1_test)

##Multivariate example
    library(fda)
    data("triangle")
    basis<- create.bspline.basis(c(1,21), nbasis=25)
    z<-sample(1:100,0.8*100,replace=FALSE)
    var1_train<-smooth.basis(argvals=seq(1,21,length.out = 101),y=t(triangle[z,1:101]),
                              fdParobj=basis)$fd
    var1_test<-smooth.basis(argvals=seq(1,21,length.out = 101),y=t(triangle[-z,1:101]),
                            fdParobj=basis)$fd
    var2_train<-smooth.basis(argvals=seq(1,21,length.out = 101),y=t(triangle[z,102:202]),
                            fdParobj=basis)$fd
    var2_test<-smooth.basis(argvals=seq(1,21,length.out = 101),y=t(triangle[-z,102:202]),
                            fdParobj=basis)$fd

    model<-funHDDC(list(var1_train,var2_train),K=3)
    pred<-predict(model,list(var1_test,var2_test))

# }
# NOT RUN {
    #Another example on Canadian data
    library(fda)

    #Clustering the "Canadian temperature" data (Ramsey & Silverman): univariate case
    daybasis65 <- create.fourier.basis(c(0, 365), nbasis=65, period=365)
    z<-sample(1:35,0.9*35,replace=FALSE)
    daytempfd_train <- smooth.basis(day.5, CanadianWeather$dailyAv[,z,"Temperature.C"],
    daybasis65,fdnames=list("Day", "Station", "Deg C"))$fd
    daytempfd_test <- smooth.basis(day.5, CanadianWeather$dailyAv[,-z,"Temperature.C"],
    daybasis65,fdnames=list("Day", "Station", "Deg C"))$fd

    model<-funHDDC(daytempfd_train,K=3)
    pred<-predict.funHDDC(model,daytempfd_test)


    #Clustering the "Canadian temperature" data (Ramsey & Silverman): multivariate case
    daybasis65 <- create.fourier.basis(c(0, 365), nbasis=65, period=365)
    z<-sample(1:35,0.9*35,replace=FALSE)
    daytempfd_train <- smooth.basis(day.5, CanadianWeather$dailyAv[,z,"Temperature.C"],
    daybasis65,fdnames=list("Day", "Station", "Deg C"))$fd
    daytempfd_test <- smooth.basis(day.5, CanadianWeather$dailyAv[,-z,"Temperature.C"],
    daybasis65,fdnames=list("Day", "Station", "Deg C"))$fd
    dayprecfd_train<-smooth.basis(day.5, CanadianWeather$dailyAv[,z,"Precipitation.mm"],
    daybasis65,fdnames=list("Day", "Station", "Mm"))$fd
    dayprecfd_test<-smooth.basis(day.5, CanadianWeather$dailyAv[,-z,"Precipitation.mm"],
    daybasis65,fdnames=list("Day", "Station", "Mm"))$fd

    model<-funHDDC(list(daytempfd_train,dayprecfd_train),K=3)
    pred<-predict.funHDDC(model,list(daytempfd_test,dayprecfd_test))
# }
# NOT RUN {
    
# }