R/pmle.norm.R

In MixtureInf: Inference for Finite Mixture Models

pmle.norm <-
function(x,m0=1,lambda=0,inival=NULL,len=10,niter=50,tol=1e-6,rformat=FALSE)
{
#x: 		data, can be either a vector or a matrix with the 1st column being the observed values
#       		and the 2nd column being the corresponding frequencies. 
#m0:	 	order of finite normal mixture model.
#lambda:	size of penalized function of mixing distribution
#inival:	initial values chosen for the EM-algorithm
#len:		number of initial values chosen for the EM-algorithm.	
#niter:     least number of iterations for all initial values in the EM-algorithm.
#tol:		tolerance value for the convergence of the EM-algorithm.
#rformat	format for output, rformat=T means the format of output is determined by R software.
#		rformat=F means the format of output is determined by our default setting. When the output is
#		larger than 0.001, it is determined by round(output,3); When the output is less than 0.001,
#		it is determined by signif(output,3).
	if (is.data.frame(x))
	{	
		if (ncol(x)==2)
			x=as.matrix(x)
		if (ncol(x)==1 | ncol(x)>2)
			x=x[,1]
	}
	if (is.matrix(x))
	{
		xx=c()
		for (i in 1:nrow(x))
			xx=c(xx,rep(x[i,1],x[i,2]))
		x=xx
	}

	out=phi.norm(x,m0,lambda,inival,len,niter,tol)
	alpha=out[[1]]
	mean=out[[2]]
	var=out[[3]]
	loglik=out[[4]]
	ploglik=out[[5]]
	
	if (rformat==F)
	{
		alpha=rousignif(alpha)
		mean=rousignif(mean)
		var=rousignif(var)
		loglik=rousignif(loglik)
		ploglik=rousignif(ploglik)
	}

	if (m0>1)
	{
		list('PMLE of mixing proportions'=alpha,
		'PMLE of means'=mean,
		'PMLE of variances'=var,
		'log-likelihood'=loglik,
		'Penalized log-likelihood'=ploglik)
	}
	else 
	{
		list('MLE of mixing proportions'=alpha,
		'MLE of means'=mean,
		'MLE of variances'=var,
		'log-likelihood:'=loglik)
	}
}