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Re: st: Wald test in Random Coefficient Model
From
Joerg Luedicke <[email protected]>
To
[email protected]
Subject
Re: st: Wald test in Random Coefficient Model
Date
Thu, 24 Feb 2011 17:04:26 -0500
On Thu, Feb 24, 2011 at 4:44 PM, Shikha Sinha <[email protected]> wrote:
> Thank you very much for your response. It is WHO "Health Behaviour in
> school-aged children " data.
>
> May I also ask you the interpretation of "LR test vs. logistic
> regression: chi2(3) = 2132.55 Prob > chi2 = 0.0000". in the
> following output.
>
> ------------------------------------------------------------------------------
> Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
> -----------------------------+------------------------------------------------
> wp5: Unstructured |
> var(drel) | .037479 .0209765 .0125136 .1122521
> var(_cons) | .1952712 .0515963 .1163391 .3277558
> cov(drel,_cons) | -.0052752 .0251633 -.0545943 .0440439
> ------------------------------------------------------------------------------
> LR test vs. logistic regression: chi2(3) = 2132.55 Prob > chi2 = 0.0000
this is a test of the multilevel model against a complete pooled
model, i.e. the logistic regression without any random effects. If you
have more than 1 random effect it basically comes down to another sort
of omnibus test and is most often not very useful. you can check it
out:
---Example---
/*get some data*/
sysuse auto, clear
/*artificially creating 10 clusters*/
xtile level2= price, n(10)
/*getting a binary dep var*/
gen mile=mpg>20
/*fitting conventional logit*/
logit mile headroom
est sto m1
/*fitting multilevel model with varying intercept and slope*/
xtmelogit mile headroom || level2: headroom
est sto m2
/*comparing fit in terms of the likelihood (aka chi2 difference test)*/
di 2*(40.648143 - 39.082619)
---End---
J.
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