--- On Mon, 27/4/09, [email protected] wrote:
> I performed an ice command that generated 5-data sets (each
> n = 3,300). I followed up with a mim run (regress). I am
> using Stata 10. I read the ice and mim command help files.
> It appears to me that I cannot obtain an R^2 value for the
> overall mim run. It also seems to me that I cannot perform
> any multivariate diagnostics for assumption violations on
> that run of the data. Is this correct?
I would limit the model checking to graphical checks (I
would do so anyhow even if I used non-imputed data, but
that is another issue and opions differ on this). An
example of how you could do that is shown below:
*---------- begin example ----------------
sysuse auto, clear
gen miss = missing(rep78, mpg, price)
ice rep78 mpg price, clear m(5)
mim, storebv: reg price mpg rep78
predict xb
gen resid = xb - price
twoway scatter resid mpg ///
if miss == 0 , ///
by(_mj, compact) || ///
scatter resid mpg ///
if miss == 1, ///
mcolor(red) ///
legend(order(1 "observed" ///
2 "imputed")) ///
ytitle(residual) || ///
mband resid mpg
*-------------- end example -----------------
As to R^2, see the opion of Don Rubin here:
http://www.mail-archive.com/[email protected]/msg00102.html
and my interpretation of it in Stata code here:
http://www.stata.com/statalist/archive/2007-04/msg00900.html
Hope this helps,
Maarten
-----------------------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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