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From |
Martina Brandt <brandt@soziologie.uzh.ch> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: r-square 4-level-logit-regression xtmelogit |

Date |
Tue, 16 Oct 2007 10:51:40 +0200 |

Dear Nick, dear list,

this is what I did - and what I got, using the same samples:

1) gllamm

xi: gllamm y x1...xn , i(l2 l3 l4) link(logit) family (binom) from(a) nip(10) adapt robust eform

/*Results (part):

Variances and covariances of random effects

------------------------------------------------------------------------------

***level 2 (l2)

var(1): 1.5149127 (.55028054)

***level 3 (l3)

var(1): 2.1240474 (.52440731)

***level 4 (l4)

var(1): .02896267 (.02613964)

*/

gllapred phat1

sum phat1

/*

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

phat1 | 7680 -2.010795 1.506836 -7.667717 3.125869

*/

2)xtmelogit

xi: xtmelogit y x1...xn || l4: || l3: || l2:, variance or

/*

------------------------------------------------------------------------------

Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]

-----------------------------+------------------------------------------------

land: Identity |

var(_cons) | .022862 .0201887 .0040499 .1290577

-----------------------------+------------------------------------------------

hhnr: Identity |

var(_cons) | 2.171575 .4617767 1.43143 3.294423

-----------------------------+------------------------------------------------

beobnr: Identity |

var(_cons) | 1.434772 .5741192 .6549036 3.143318

------------------------------------------------------------------------------

*/

predict phat2

sum phat2

/*

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

phat2 | 7680 .2411294 .2457556 .0005897 .9551997

*/

-> the ("explained") variance (SD(phat2)^2) now is much smaller compared to the overall variance than in the first example using gllamm. I must have overlooked something important, but I have no idea, what that could be? is it that predict (option mu) produces somethind different to gllapred? would i have to take only the fixed part (predict, xb)?

by the way: the example in the help function also indicates the level-1-indicator, but this produces the same results, just takes a lot longer. Does anyone know more about that?

Sorry for bothering you that extensively!

martina

_________________________

Martina Brandt

Universität Zürich

Soziologisches Institut

Andreasstr. 15

CH-8050 Zürich

Tel. +41(0)44 6352347

www.suz.uzh.ch/ages

Nick Cox schrieb:

If you can demonstrate that results that should be the same, from the same data and the same model, are different from -xtmelogit- and -gllamm-, then there is a problem.

I think you need at least to show (examples of) the commands you used and the results you got for experts on these commands to comment.

Nick n.j.cox@durham.ac.uk

Martina Brandt

Dear Nic, thanks a lot for your answer - i know that pseudo-r-squares are quite tricky. But the probem here is, that the same pseudo-r-square changes using xtmemixed instead of gllamm because the estimated variance of phat in comparison to the level 1 to level 4 variances is much smaller than it is using gllamm?!

Martina BrandtOn Mon, 15 Oct 2007 17:03:56 +0100 "Nick Cox" <n.j.cox@durham.ac.uk> wrote:There is a entire bestiary of pseudo-R-squares

based on different kinds of analogy to R-square, strong, weak and otherwise. There is no

reason in general why they should agree.

regression,mc kelvey and zavoina suggest an r2 for multilevel logitdivided by thewhich is the variance of the predicted probabilitiesidea, whytotal variance of the model (=proportion of explained variance). in the four level model this would be (var(phat))/((var(phat)+((pi2)/3))+var(level2)+var(level3)+var

(level4))

(see snijders & bosker 1999: 225).

using gllamm i always had pseudo r2 around 0.20, and now using xtmelogit it is supposed to be only around 0.01. does anyone have anthis could have happened and how these differences couldbe explained?* * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

* * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**RE: st: RE: r-square 4-level-logit-regression xtmelogit***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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