Consider calling glm.fit() instead of glm()

[mbojan]

In

ergm/R/ergm.mple.R

Lines 100 to 101 in 1f4401e

	glm.result <- quietly(function() glm(pl$zy ~ .-1 + offset(pl$foffset),
	data=data.frame(pl$xmat), weights=pl$wend, family=family))()

consider calling glm.fit() directly rather than glm(). Experiments with biggish data show that it might cut the computing time by half.

[krivit]

@mbojan , can you test to see if this works?

[krivit]

@mbojan , I'll be submitting an update to ergm in the next few days. If you want this to go in, let me know ASAP.

[mbojan]

Oh dear, I have a week of workshops, including ergms. Can we release next week? The principle answer is yes, but haven't tested yet.

[krivit]

Oh dear, I have a week of workshops, including ergms. Can we release next week? The principle answer is yes, but haven't tested yet.

OK, can you get it done in the next day or two?

[krivit]

@mbojan ?

[krivit]

@AdrienLeGuillou , you often fit MPLE to large networks, right? Can you by any chance test this?

[AdrienLeGuillou]

I just ran a quick test on a smaller 10k nodes network using this branch. It worked fine. I can't tell if it was faster or not as I usually work with "Stochastic-Approximation" on these smaller local tests.
I can try on the HPC with our 3 - 100k nodes networks and compare the time it takes.

[AdrienLeGuillou]

I just realized that ergm.mple is called whatever the main.method we use.
Therefore I can confirm that it works perfectly on our 10k nodes networks.
It takes a very similar amount of time to fit the networks with both version as the MPLE step is not the longest part anyways.

[AdrienLeGuillou]

I confirm it also works on the 100k nodes network.
It was actually longer with glm.fit. But the difference was on the number of MCMLE iterations.

[mbojan]

Thanks @AdrienLeGuillou . @krivit don't merge, leave as is. I need to dig out the script where I think I noticed the difference.