Guidance on HLM/multilevel modeling

[jpkelley]

This thread should receive an award for the speed at which it reached 40+ replies.

I second the vote for lme4 (and the newer glmmADMB, which handles zero-inflated GLMMs). The glmmADMB option is great, but it is still in development. So, for zero-inflated data, there's no R option (that I know about) for GLMMs. You have to do this manually by splitting the data. The Zuur book that Jake mentions goes into this a bit. Their new book, which is specifically about zero-inflated models, supposedly goes into more detail.

That said, the Zuur book on mixed-effects models that Jake mentioned was my primary reference when I was learning GLMMs and GAMMs, and I highly recommend it. Changed my life, I tell you. I've become an annoying addict of additive models as a result. I think I should charge Zuur for the therapy sessions.

Also, there's a good website that seems to keep up with discussions and new issues about GLMMs, etc. It might be too field-specific for most people:
http://glmm.wikidot.com/start

[noetsi]

Threads like this remind me how little I really understand statistics as I wonder how you know if between group variation is signficant (and struggle with that) and then I read about zero-inflated data and GLMM's and GAMM's - things I have never heard about and probably won't.

Oh well exepectations for running data is much more limited where I work than that in all liklihood

[jpkelley]

Isn't life good when the fact that something isn't there (0) or isn't doing something (0, again) is at once interesting? There's data everywhere, even where there's not. Yes, that bit of zen just happened. Go ponder THAT on this here Friday evening.

[trinker]

I received numerous resources and suggestions tonight and am marking this thread as solved. I am grateful and will most likely delve into this seriously at the end of the semester. I still think I want to take an online class in addition to learning it on my own.

[Lazar]

The thing to keep in mind is that the limit of mlm models as variance in l2 approaches 0 or ininity is the complete pooling (ignore the l2 varaibles completely) and non-pooling OLS approaches repsectively. Both these approaches can be useful on occasion but given these limits, for the most part, you might as well fit a multilevel model when the data structure is known (a good discussion can be found here).

I think the point though noetsi, is that in OLS we might not have indicators or knowledge of the multilevel structure or may have good reason not to think that the multilevel structure will be an influence so we can only model the data that we have. Other times we may only have l1 hypotheses so we are only interested in ensuring that the SEs are appropriate and so typical regression approaches with TYPE=COMPLEX in mplus or a custom block bootstrap in r is perfectly acceptable.

[trinker]

For continuing completeness:

http://lme4.r-forge.r-project.org/book/