How to compute the log-likelihood of the LDA model

问题:

I am typical, regular, everyday R user. In R there is very helpful lda.collapsed.gibbs.sampler in lda package tha uses a collapsed Gibbs sampler to fit a latent Dirichlet allocation (LDA) model and returns point estimates of the latent parameters using the state at the last iteration of Gibbs sampling.

This function also has a great parameter compute.log.likelihood which, when set to TRUE, will cause the sampler to compute the log likelihood of the words (to within a constant factor) after each sweep over the variables. This is useful for assessing convergence and in comparing different LDA models (computeted for different number of topics).

I am interested if there is such an option in vowpal_wabbit's LDA model?