R/print.gagafit.r In gaga: GaGa hierarchical model for high-throughput data analysis

Documented in print.gagafit

```print.gagafit <- function(x,...) {

if (x\$nclust==1) cat("GaGa hierarchical model.") else cat("MiGaGa hierarchical model (",round(x\$nclust)," clusters.",sep="")
if (x\$method=='EM') cat(" Fit via Expectation-Maximization\n") else if (x\$method=='quickEM') cat("Fit via quick Expectation-Maximization\n") else if (x\$method=='Gibbs') cat(" Fit via Gibbs sampling (",nrow(x\$mcmc)," iterations kept)\n",sep="") else if (x\$method=='MH') cat(" Fit via Metropolis-Hastings sampling (",nrow(x\$mcmc)," iterations kept)\n",sep="") else if (x\$method=='SA') cat(" Fit via Simulated Annealing (",nrow(x\$mcmc)," iterations)\n",sep="")
if (x\$equalcv) cat("Assumed constant CV across groups \n") else cat("Assumed varying CV across groups\n")

if (is.null(x\$pp)) {
cat("  ",ncol(x\$patterns)," groups, ",nrow(x\$patterns)," hypotheses (expression patterns)\n\n",sep="") } else {
cat("  ",nrow(x\$pp)," genes, ",ncol(x\$patterns)," groups, ",nrow(x\$patterns)," hypotheses (expression patterns)\n\n",sep="")
}
cat("The expression patterns are\n")
if (sum(is.na(x\$parest))==0) { probpat <- getpar(x)\$probpat } else { probpat <- NA }
print(x\$patterns,probpat)
cat("\n")
if (!is.na(x\$parest[1])) {
cat("Hyper-parameter estimates\n\n")
par <- getpar(x)
cat("  ",names(x\$parest)[1:(2+2*x\$nclust)],"\n")
cat("  ",round(par\$a0,3),round(par\$nu,3),round(par\$balpha,3),round(par\$nualpha,3),"\n\n")
cat("  ",names(x\$parest)[(2+2*x\$nclust+1):(2+3*x\$nclust)],"\n")
cat("  ",round(par\$probclus,3),"\n\n")
} else {
cat("Hyper-parameter estimates not computed yet. Run function parest.\n")
}
if (is.null(x\$pp)) { cat("Posterior probabilities not computed yet. Run function parest.\n") }
}
```

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gaga documentation built on May 2, 2018, 3:35 a.m.