# Re: st: GLLAMM help

 From "Dana Shills" <[email protected]> To [email protected] Subject Re: st: GLLAMM help Date Sat, 02 Oct 2004 23:52:16 +0000

Stas

Size refers to firm sizes categories(small/medium/large)..I have observations on firms that are classified into size groups as well as industries. So i am trying to see how much of the variance inY is explained by variance due to country and size(or industry) (and their interactions as well).

In the total variance you mention, what is _pi^2/6? Im sorry if this is too basic a question but could you at least direct me to some elementary reference material..I have only used SAS's PROC MIXED for this kind of variance component analysis before.

_pi^2/6 + 0.054 + 0.012.

Thanks

Dana

On Sat, 02 Oct 2004 22:24:15 +0000, Dana Shills <[email protected]> wrote:

```How do you obtain the residual variance in a model? Im trying to see what
percentage of total variance is explained by the variables in the model. So
for instance, when I run:

I obtain

Variances and covariances of random effects
------------------------------------------------------------------------------
***level 2 (size)

var(1): .05399151 (.04772325)

***level 3 (country)

var(1): 0.0116962 (0.0113)
------------------------------------------------------------------------------

So is residual variance(ie unexplained variance)
just=1-(0.05399151)-(0.0116962)?
```
No. There are no natural concepts of total or explained variances
outside of linear regression model. The primary use of the reported
variance is to test the hyporhesis that the variance is equal to zero,
and there is no effect at this level. For your results, this seems to
be the case: neither the country nor the size seem to matter. This is
a good news: you don't have to complicate things and -gllamm- them;
the built-in -ologit- will do.

Besides, those variances add to each other, so the total variance is
_pi^2/6 + 0.054 + 0.012.

By the way, what does the size mean in your model?

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