WOMBAT employs iterative schemes to locate the maximum of the (log) likelihood function. The program stops when a certain maximum number of iterates have been carried out or when it considers changes between iterates to be sufficiently small.
The default convergence criteria employed are fairly stringent. Hence, WOMBAT may stop after the maximum number of iterates allowed with the warning message that full convergence has not been achieved. The question then is whether this is really the case or whether this is overzealous and that – for practical purposes – the analysis has converged after all. Alternatively, WOMBAT may not have reached the maximum number of iterations but progress (i.e. increase in log likelihood) is small and we want to decide whether to stop the run or to continue.
WOMBAT reports a number of statistics aimed at helping to make this decision. Places to look are:
Iterateswritten out during the analysis
Sum⋅Estimates.outgenerated when WOMBAT stops.
Characteristics to look at (when using the AI algorithm) are:
If all this is inconclusive, a continuation run – specifying a only few iterates and using another maximisation algorithm than the one used previously – is highly recommended !