Hi there,
I'm sorry to bother you with something so trivial but I am a new Stata user and I'm stumped on a simple problem. If you coul spare a moment I'd be very grateful. I'm trying to delete variable values of "99" from my dataset and each time I use the command:
Drop if w1==99
participants who have answered "99" on the survey are dropped from the study entirely. By the time I've dropped all of the "99"s for each variable I've lost almost all of my data. I would like to eliminate a participant's "99" answer on a single variable without eliminating them from the study.
I am writing to you because I've seen your name on statalist often - and because I don't yet know how to address an inquiry to statalist. Thanks for your time and enjoy the weekend!!
- Andie
(I also receive email at [email protected].)
>>> [email protected] 08/14/03 07:04PM >>>
----- Original Message -----
From: "Ralf Dewenter" <[email protected]>
To: <[email protected]>
Sent: Thursday, August 14, 2003 12:44 PM
Subject: st: multivariate count regression
> Hi all,
>
> Does anybody know, where to find an ado-file
> for multivariate count regressions?
> Thanks.
>
> Regards
>
> Ralf Dewenter
>
Would -poisson- or -nbreg- with -suest- be sufficient? I believe it is similar
to -mvreg- in that it estimates the between-equation covariance so you can test
coefficients across equations.
Example:
. clear
. set obs 100
obs was 0, now 100
. gen x1 = invnorm(uniform())
. gen x2 = invnorm(uniform())
. gen xb = 1 + 2*x1 - x2
. genpoisson y, xbeta(xb)
. genpoisson y2, xbeta(xb)
. qui poisson y x1 x2,score(s1)
. est store A
. qui poisson y2 x1 x2, score(s2)
. est store B
. suest A B
Simultaneous results for A, B
Obs = 100
------------------------------------------------------------------------------
| Robust
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
A_y |
x1 | 1.432079 .1199582 11.94 0.000 1.196966 1.667193
x2 | -.7417589 .0804557 -9.22 0.000 -.8994491 -.5840686
_cons | 1.45254 .0961629 15.10 0.000 1.264064 1.641015
-------------+----------------------------------------------------------------
B_y2 |
x1 | 1.418806 .1223085 11.60 0.000 1.179085 1.658526
x2 | -.7914187 .0901748 -8.78 0.000 -.968158 -.6146794
_cons | 1.459112 .097084 15.03 0.000 1.26883 1.649393
------------------------------------------------------------------------------
. test [A_y]x2 = [B_y2]x2, common
( 1) [A_y]x2 - [B_y2]x2 = 0
chi2( 1) = 1.48
Prob > chi2 = 0.2236
Scott
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