Re: st: comparing nested models after multiple imputation

[Richard Williams <richardwilliams.ndu@gmail.com>]

At 08:08 PM 5/20/2012, Chelsea Garneau wrote:
> The problem is that there is no likelihood ratio test after multiple
> imputation.  All I have to go on is an F statistic for each
> model.....could I just compare with a chi-square test, maybe?  Not
> sure what the df would be......but I've read that the F statistic is
> supposed to be comparable to the Wald chi-square test.....so maybe it
> would just be 1 df?

See -help mi test- . Basically you run the unconstrained model and 
then test whether, say, some subset of coefficients is zero. So, for 
example, you could do something like

mi estimate: logit y x1 x2 x3 x4 x5 x6
mi test x4 x5 x6

That will test whether any of the estimated effects of x4, x5 and x6 
significantly differ from zero.

If you weren't doing mi and wanted to instead do LR tests, you would 
do something like

logit y x1 x2 x3
est store constrained
logit y x1 x2 x3 x4 x5 x6
est store unconstrained
lrtest constrained unconstrained

> New Question:  I'm also trying to figure out how to calculate my own
> beta's for multilevel linear regressions....this option is also not
> available in Stata from what I've read so far.  I know that betas are
> supposed to be the coefficient X (standard error of the independent
> variable/ standard error of the dependent variable).  But where I do I
> find the S.E. of the dependent (this might be a really silly
> question).  I can obviously get the standard deviation of the variable
> overall, but is that the same as the S.E. in the regression?

findit mibeta

However, since you say multilevel, I don't know if this will do what 
you want; some additional work may be needed.


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Richard Williams, Notre Dame Dept of Sociology
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