R/MGHD.R

In MixGHD: Model Based Clustering, Classification and Discriminant Analysis Using the Mixture of Generalized Hyperbolic Distributions

MGHD <- function(data=NULL, gpar0=NULL, G=2, max.iter=100, label =NULL , eps=1e-2, method="kmeans" ,scale=TRUE ,nr=10, modelSel="AIC",seed=12345) {
##Expexctation Maximization estimation of GHD
##data
## G n clusters
##n number of iterations
  data=as.matrix(data)
if( scale==TRUE){
	data=scale(as.matrix(data))}
    pcol=ncol(data)
    #if (nrow(data)<((G-1)+G*(2*pcol+2+pcol*(pcol-1)/2)))stop('G is too big, number of parameters > n')
	if (is.null(data)) stop('data is null')
	if (nrow(data) == 1) stop('nrow(data) is equal to 1')
	if (any(is.na(data))) stop('No NAs allowed.')
	if (is.null(G)) stop('G is NULL')
	#if ( G < 1) stop('G is not a positive integer')
	if ( max.iter < 1) stop('max.iter is not a positive integer')
	if(!is.null(seed)) set.seed(seed)

     if(modelSel=="BIC"){
    bico=-Inf
    t=length(G)
    BIC=matrix(NA,t,1)
    cont=0
	for(b in 1:t){
        mo=try(mainMGHD(data=data, gpar0=gpar0, G=G[b], n=max.iter, eps=eps,  label=label,method= method,nr=nr),silent = TRUE)
        cont=cont+1
        if(is.list(mo)){
            bicn=mo$BIC
            BIC[cont]=bicn}
        else{bicn=-Inf
            BIC[cont]=NA}
        if(bicn>bico){
            bico=bicn
            sg=G[b]
            model=mo
        }
    }
    val=list(index=BIC,model=model)
    cat("The best model (BIC) for the range of  components used is  G = ", sg,".\nThe BIC for this model is ", bico,".",sep="")
         return(val)}
     
     
      else if(modelSel=="ICL"){
          bico=-Inf
          t=length(G)
          ICL=matrix(NA,t,1)
          cont=0
          for(b in 1:t){
              mo=try(mainMGHD(data=data, gpar0=gpar0, G=G[b], n=max.iter, eps=eps,  label=label,method= method,nr=nr),silent = TRUE)
              cont=cont+1
              if(is.list(mo)){
                  bicn=mo$ICL
                 ICL[cont]=bicn}
              else{bicn=-Inf
                  ICL[cont]=NA}
              if(bicn>bico){
                  bico=bicn
                  sg=G[b]
                  model=mo
              }
          }
          val=list(index=ICL,model=model)
          cat("The best model (ICL) for the range of  components used is  G = ", sg,".\nThe ICL for this model is ", bico,".",sep="")
          return(val)}
      else if(modelSel=="AIC3"){
          bico=-Inf
          t=length(G)
          AIC3=matrix(NA,t,1)
          cont=0
          for(b in 1:t){
              mo=try(mainMGHD(data=data, gpar0=gpar0, G=G[b], n=max.iter, eps=eps,  label=label,method= method,nr=nr),silent = TRUE)
              cont=cont+1
              if(is.list(mo)){
                  bicn=mo$AIC3
                  AIC3[cont]=bicn}
              else{bicn=-Inf
                 AIC3[cont]=NA}
              if(bicn>bico){
                  bico=bicn
                  sg=G[b]
                  model=mo
              }
          }
          val=list(index=AIC3,model=model)
          cat("The best model (AIC3) for the range of  components used is  G = ", sg,".\nThe AIC3 for this model is ", bico,".",sep="")
          return(val)}
      else {
          bico=-Inf
          t=length(G)
          AIC=matrix(NA,t,1)
          cont=0
          for(b in 1:t){
              mo=try(mainMGHD(data=data, gpar0=gpar0, G=G[b], n=max.iter, eps=eps,  label=label,method= method,nr=nr),silent = TRUE)
              cont=cont+1
              if(is.list(mo)){
                  bicn=mo$AIC
                  AIC[cont]=bicn}
              else{bicn=-Inf
                  AIC[cont]=NA}
              if(bicn>bico){
                  bico=bicn
                  sg=G[b]
                  model=mo
              }
          }
          val=list(index=AIC,model=model)
          cat("The best model (AIC) for the range of  components used is  G = ", sg,".\nThe AIC for this model is ", bico,".",sep="")
          return(val)}
}