OBIC: Calculates BIC, AIC, PLS, log-likelihood

In NHMM: Bayesian Non-Homogeneous Markov and Mixture Models for Multiple Time Series

Description

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.

Usage

OBIC(nhmmobj, outfile = NULL)

Arguments

nhmmobj	an object created from the NHMM function
outfile	a directory to put the .png plot

Details

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).

Value

BIC

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) 

NHMM documentation built on July 1, 2020, 7:28 p.m.