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## File Name: fuzcluster.R
## File Version: 0.163
#*** Clustering for continuous fuzzy data
fuzcluster <- function(dat_m, dat_s, K=2, nstarts=7,
seed=NULL, maxiter=100, parmconv=.001, fac.oldxsi=0.75, progress=TRUE )
{
s1 <- Sys.time()
dev0 <- 1e200
dat_resp <- 1 - is.na(dat_m)
if ( ! is.null(seed) ){
nstarts <- 1
}
for ( rr in 1:nstarts ){
res1 <- fuzcluster_estimate(K, dat_m, dat_s, dat_resp,
maxiter=maxiter, parmconv=parmconv, progress=progress,
seed=seed, fac.oldxsi=fac.oldxsi)
if ( res1$deviance < dev0 ){
res <- res1
dev0 <- res1$deviance
}
}
s2 <- Sys.time()
### end random starts
### computation of information criteria
dev <- res$deviance
ic <- list( deviance=dev, n=nrow(dat_m) )
I <- ncol(dat_m)
ic$np <- (K-1) + 2*K*I
ic$AIC <- dev + 2*ic$np
ic$BIC <- dev + ( log(ic$n) )*ic$np
ic$CAIC <- dev + ( log(ic$n) + 1 )*ic$np
ic$AICc <- ic$AIC + 2*ic$np * ( ic$np + 1 ) / ( ic$n - ic$np - 1 )
res$ic <- ic
res$K <- K
res$s1 <- s1
res$s2 <- s2
res$nstarts <- nstarts
class(res) <- "fuzcluster"
return(res)
}
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