Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down at the end of May, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Stas Kolenikov <skolenik@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: xtmelogit: comparing models |

Date |
Fri, 5 Oct 2012 15:37:50 -0500 |

I think you would be better off with Wald test that you've outlined. Likelihood ratio test has a better finite sample performance, but only when the model is correctly specified. Information criteria are kind of goofy, given that you have different amount of information (effective sample sizes) for different parameters in mixed models. The FAQ you cited is somewhat related: your data are not i.i.d., so appropriate likelihood ratio testing can get complicated. Wald test is more robust, and only requires the vce matrix to be correctly estimated (which is much easier to achieve). You would need to figure out which levels of your factor variables were indeed estimated, depending on how exactly the factor variables were broken down into the baselines. Your -test- command does seem about right, provided that these are indeed the right levels of group and condition. -- -- Stas Kolenikov, PhD, PStat (SSC) :: http://stas.kolenikov.name -- Senior Survey Statistician, Abt SRBI :: work email kolenikovs at srbi dot com -- Opinions stated in this email are mine only, and do not reflect the position of my employer On Fri, Oct 5, 2012 at 10:50 AM, Luca Campanelli <l.campanelli@yahoo.it> wrote: > Dear Stata users, > I’d like to fit and compare mixed effects logistic regression models with crossed random effects using the function xtmelogit (Stata 12IC for Windows). > > For example (“group” has 2 levels[0,1] and “condition” has 3 levels[1,2,3]): > (1) xtmelogit resp i.group i.condition , || _all: R.item, covariance(id) || sbj: , covariance(id) > (2) xtmelogit resp i.group i.condition i.group#i.condition , || _all: R.item, covariance(id) || sbj: , covariance(id) > > In comparing two models, I found a big discrepancy between lrtest on one side, and AIC-BIC on the other side. lrtest was highly significant, indicating that (2) was better than (1), while AIC and BIC values were clearly smaller for model (1). > Which should I trust? > > Does this apply to my case http://www.stata.com/support/faqs/statistics/likelihood-ratio-test/ ? > If yes, how can I do the Wald test? > Would it be: > test 1.group#2.condition 1.group#3.condition > > Is it correct? I saw others using testparm or lincom. > I would appreciate any help to understand what the appropriate thing to do is. > > thank you, > Luca > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: xtmelogit: comparing models***From:*Luca Campanelli <l.campanelli@yahoo.it>

- Prev by Date:
**Re: st: Merging Do-Files** - Next by Date:
**st: RE: RE: Using the predictnl command following a model containing restricted cubic splines and time-dependent effects** - Previous by thread:
**Re: st: xtmelogit: comparing models** - Next by thread:
**Re: st: xtmelogit: comparing models** - Index(es):