R/MSGHD.R

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

MSGHD <- function(data=NULL, gpar0=NULL, G=2, max.iter=100, label =NULL ,eps=1e-2, method="km",scale=TRUE,nr=10, modelSel="AIC",seed=12345 ) {
    data=as.matrix(data)
    if( scale==TRUE)
    {data=scale(data)}
    pcol=ncol(data)
    #  if (nrow(data)<((G-1)+G*(4*pcol+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 (ncol(data) == 1) stop('ncol(data) is equal to 1; This function currently only works with multivariate data p > 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=mainMSGHD(data=data, gpar0=gpar0, G=G[b], n=max.iter, eps=eps,  label=label,method= method,nr=nr)
        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(mainMSGHD(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$ICL
                ICL[cont]=bicn}
            else{bicn=-Inf
                ICL[cont]=NA}
            if(bicn>bico){
                bico=bicn
                sg=G[b]
                model=mo
            }
        }
        val=list(index=ICL,model=model)
        cat("The best model (ICL) for the range of  components used is  G = ", sg,".\nThe ICL for this model is ", bico,".",sep="")
        return(val)}
    else if(modelSel=="AIC3"){
        bico=-Inf
        t=length(G)
        AIC3=matrix(NA,t,1)
        cont=0
        for(b in 1:t){
            mo=try(mainMSGHD(data=data, gpar0=gpar0, G=G[b], n=max.iter, eps=eps,  label=label,method= method,nr=nr),silent = TRUE)
            cont=cont+1
            if(is.list(mo)){
                bicn=mo$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=mainMSGHD(data=data, gpar0=gpar0, G=G[b], n=max.iter, eps=eps,  label=label,method= method,nr=nr)
            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)}



}