crashes due to L=-INF on larger datasets

[stephens999]

we're running maptpx on some larger datasets. When we do this we get infinite L sometimes in this line:

if(is.infinite(L)){ L <- sum( (log(Q0)*col_sums(X))[Q0>0] )}

but the problem is that the sum() returns NA.
This then causes a crash later at
if(QNup$L < L){
because L is NA.

We tried to fix this by:
i) comment out the if(is.infinite(L)) line
ii) comment out reldif # reldif <- dif/L
iii) change tolerance check to dif instead of reldif: if(abs(dif) < tol){

This seemed to work except that it crashed (presumably after 1000 iterations) at
if(((iter+1)%%1000)==0){
cat(sprintf("p %d iter %d diff %g\n",
nrow(theta), iter+1,round(diff)))

due to the bug round(diff) should be round(dif).

We've fixed that and are now re-running - currently waiting for results.

What do you think? I think that the tolerance check should be on the difference in loglikelihood anyway - the relative change in loglikelihood is nonsense (at least so I always tell my students!)
But maybe there is also something to understand about why that sum() produces NA?

We can prepare a formal pull request with these changes if you like.

[mataddy]

your fix makes a ton of sense; i don't know why that diff -> dif bug wasn't causing problems before; I'll fix it now.

i agree that checking for convergence on the raw log-likelihood is the right thing to do. I used to do it that way (and argue for it in my paper at http://jmlr.csail.mit.edu/proceedings/papers/v22/taddy12/taddy12.pdf) but switched to looking at relative differences because that's how most of the other packages out there work (especially the lda package). I have no strong feelings about it and could easily switch back though; keep me posted on whether you get reasonable results. Note that of course if you keep the tolerance the same you're going to need to run for many more iterations to converge for real rather than relative log LHD difference.

[mataddy]

i merged the pull in #2 and just pushed a small doc update. thanks for this!