Nothing

```
################################################################
## Copyright 2014 Tracy Holsclaw.
## This file is part of NHMM.
## NHMM is free software: you can redistribute it and/or modify it under
## the terms of the GNU General Public License as published by the Free Software
## Foundation, either version 3 of the License, or any later version.
## NHMM is distributed in the hope that it will be useful, but WITHOUT ANY
## WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
## A PARTICULAR PURPOSE. See the GNU General Public License for more details.
## You should have received a copy of the GNU General Public License along with
## NHMM. If not, see <http://www.gnu.org/licenses/>.
#############################################################
#' Calculates BIC, AIC, PLS, log-likelihood
#'
#' \code{OBIC} calculates BIC, AIC, approximate log-likelihood and plots the
#' log-likelihood for all iterations. The log-likelihood plot should
#' be flat to show convergence to a stationary distribution. Minimize
#' the AIC and BIC for the *best* model and maximize PLS. The log likelihood is approximate in that
#' it is calculated by marginalizing over the current chain of hidden states instead
#' of using a recurrsive algorithm to compute it; every iterations produces an estimation
#' of the log-likelihood. If yhold is provided the preditive log score (PLS) is also
#' given.
#'
#' Predictive Log Score: mean(log( E(p(yhold|...))) The expectation is over all of the
#' iterations of the algorithm. And the mean is over the pT count of yhold. The scale of
#' the PLS is in the unit of t (usually days).
#'
#' @param nhmmobj an object created from the NHMM function
#' @param outfile a directory to put the .png plot
#' @return BIC
#' @return output: AIC, BIC, PLS [if yhold data was provided], log-likelihood to the GUI and a plot of the log-likelihood
#' @examples #OBIC(my.nhmm)
OBIC=function(nhmmobj, outfile=NULL)
{
T=nhmmobj$T
J=nhmmobj$J
K=nhmmobj$K
B=nhmmobj$B
A=nhmmobj$A
iters=nhmmobj$iters
burnin=nhmmobj$burnin
outboo=nhmmobj$outboo
outdir=nhmmobj$outdir
loglik=nhmmobj$loglik
BICp=nhmmobj$BICp
PLS=nhmmobj$PLS
L=B+K
BICf=-2*max(loglik)+BICp*log(T)
AICf=-2*max(loglik)+2*BICp
print(paste("Parameter count: ",BICp,sep=""))
print(paste("BIC: ",round(BICf,2), " (favored method)",sep=""))
print(paste("AIC: ", round(AICf,2),sep=""))
if(!is.null(PLS)){print(paste("PLS: ",round(PLS,2)))}
if(!is.null(outfile)){ png(paste(outfile,"BIC.png",sep=""), width=300, height=300)}
plot(loglik, xlab="Iterations", ylab="Log-likelihood")
if(!is.null(outfile)){ dev.off()}
BICf
}
```

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