Nothing
fm.smn.cr <- function(cc, y, x1, Abetas = NULL, medj= NULL, sigma2 = NULL, pii = NULL, nu=NULL, g = NULL, family = "Normal", error = 0.00001, iter.max = 100, aitken = TRUE)
{
#Running the algorithm
#Abetas = NULL; medj= NULL; sigma2 = NULL; pii = NULL; nu=NULL; g = 2; get.init = TRUE; criteria = TRUE; group = FALSE; family = "T"; error = 10^-6; iter.max = 100; obs.prob= FALSE; kmeans.param = NULL;aitken = TRUE
out <- CensMixRegEM(cc, y, x1, Abetas, medj, sigma2, pii, nu, g, family, error, iter.max, aitken)
#show result
cat('\n')
cat('---------------------------------------------------------\n')
cat(' Censored linear mixture regression model \n')
cat('---------------------------------------------------------\n')
cat('\n')
cat('Observations =',length(y))
cat('\n')
cat('-----------\n')
cat('Estimates\n')
cat('-----------\n')
cat('\n')
#tttable <- data.frame(out$ttable,c(EP,0))
#colnames(tttable) <- c("Estimate","SE")
print(round(out$ttable,5))
cat('------------------------\n')
cat('Model selection criteria\n')
cat('------------------------\n')
cat('\n')
critFin <- c(out$loglik, out$aic, out$bic, out$edc)
critFin <- round(t(as.matrix(critFin)),digits=3)
dimnames(critFin) <- list(c("Value"),c("Loglik", "AIC", "BIC","EDC"))
print(critFin)
cat('-------\n')
cat('Details\n')
cat('-------\n')
cat('\n')
cat('Iterations =',out$iter)
cat('\n')
cat("Processing time =",out$time,units(out$time))
cat('\n')
res <- list(Abetas = out$Abetas, medj=out$medj, sigma2=out$sigma2, pii=out$pii, nu=out$nu, ttable = out$table, u0=out$u0, u1=out$u1, u2=out$u2, loglik=out$loglik, AIC=out$aic, BIC=out$bic, EDC=out$edc, time = out$time, iter=out$iter, convergence=out$convergence)
obj.out <- list(res = res)
class(obj.out) <- family
return(obj.out)
}
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