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RE: st: Variance decomposition


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Variance decomposition
Date   Sun, 11 Mar 2007 18:02:54 -0000

Stata itself can help you in these queries. 

. search variance components

yields, among other pointers, 

[XT]    xtmixed . . . . . . . . . . Multilevel mixed-effects linear regression
        (help xtmixed)

SJ-6-1  st0095  . . . . . . . . . . .  Estimating variance components in Stata
        . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. Marchenko
        Q1/06   SJ 6(1):1--21                                    (no commands)
        describes using xtmixed to estimate variance components
        in linear models

SJ-4-4  st0077  . . CIs for the variance comp. of random-effects linear models
        (help xtvc if installed)  . . . . . . . . . .  M. Bottai and N. Orsini
        Q4/04   SJ 4(4):429--435
        confidence intervals for the variance components of
        random-effects linear regression models.

In addition, check out -gllamm-. 

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

Michael Crain
 
> This methodolgy is different from ANOVA.
> 
> Under a variance components approach, the variance of each of 
> the factors is 
> independently generated by a random process.  Once the 
> variance of a factor 
> has been estimated, it remains fixed for the remainder of the 
> analysis.  The 
> variance components approach uses zero means and constant but unknown 
> variances for all the factors.  (Only the error term meets 
> this criteria 
> under ANOVA.)  Therefore, each factor can be disentangled 
> from the other 
> factors to explain each one independent of the others.  The 
> substance of 
> this approach is to explain that the differences among the 
> factor variances 
> are natural and independent of the other effects.  The 
> ultimate result of 
> this analysis is expressed as a measure of relative 
> importance of each 
> factor.  ANOVA allows us to test statistical significance but 
> does not allow 
> one to find relative importance.

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