CNprep (version 1.0)

consolidate:

Usage

consolidate(emfit, minover)

Arguments

emfit

minover

Value

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (emfit, minover) 
{
    newem <- list(mu = emfit$parameters$mean, pro = emfit$parameters$pro, 
        z = emfit$z, groups = matrix(nrow = length(emfit$parameters$mean), 
            ncol = length(emfit$parameters$mean), data = 0), 
        ngroups = length(emfit$parameters$mean))
    diag(newem$groups) <- 1
    while (newem$ngroups > 1) {
        avz <- (t(newem$z)/colSums(newem$z)) %*% newem$z
        diag(avz) <- 0
        if (max(avz) > minover) {
            g1 <- col(avz)[rev(order(avz))][1]
            g2 <- row(avz)[rev(order(avz))][1]
            gl <- min(g1, g2)
            gr <- max(g1, g2)
            newem$z[, gl] <- newem$z[, gl] + newem$z[, gr]
            newem$z <- matrix(ncol = ncol(newem$z) - 1, nrow = nrow(newem$z), 
                data = newem$z[, -gr])
            newem$mu[gl] <- (newem$mu[gl] * newem$pro[gl] + newem$mu[gr] * 
                newem$pro[gr])/(newem$pro[gl] + newem$pro[gr])
            newem$mu <- newem$mu[-gr]
            newem$pro[gl] <- newem$pro[gl] + newem$pro[gr]
            newem$pro <- newem$pro[-gr]
            newem$groups[gl, ] <- newem$groups[gl, ] + newem$groups[gr, 
                ]
            newem$groups <- matrix(ncol = ncol(newem$groups), 
                nrow = newem$ngroups - 1, data = newem$groups[-gr, 
                  ])
            newem$ngroups <- newem$ngroups - 1
        }
        else break
    }
    return(newem)
  }