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st: Re: offsets


From   "Scott Merryman" <smerryman@kc.rr.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Re: offsets
Date   Tue, 22 Jul 2003 19:42:11 -0500

----- Original Message -----
From: <paul.silcocks@sth.nhs.uk>
To: <statalist@hsphsun2.harvard.edu>
Sent: Tuesday, July 22, 2003 6:38 AM
Subject: st: offsets


> Is it possible to have more than one offset in a logistic (or other)
> regression model?
>
> Specifically I have an ordered categorical variable representing levels of a
> clinical scoring system.  I want to see what the added value of a new
> variable is, but I want to constrain the regression coefficients for the
> levels of the categorical variable to be unity.
>
> these are approaches I tried (dep is the ordered categorical variable) -
> none of which work:
>
> xi: logistic outcome1 i.dep ageatmi, offset(i.dep)
>
> logistic outcome1  _Idep_1 _Idep_2 _Idep_3 _Idep_4 ageatmi, offset( _Idep_1
> _Idep_2 _Idep_3 _Idep_4)
>
> logistic outcome1  _Idep_1 _Idep_2 _Idep_3 _Idep_4 ageatmi, offset( _Idep_1)
> offset( _Idep_2) offset( _Idep_3) offset( _Idep_4)
>
> Any thoughts?
>
> Paul Silcocks MSc, BM BCh, FRCPath, MFPHM, CStat


You could use -mlogit- with constraints to estimate a logit model with multiple
offsets.

Example

. use "C:\Stata8\auto.dta", clear
(1978 Automobile Data)

. xi i.rep
i.rep78           _Irep78_1-5         (naturally coded; _Irep78_1 omitted)

. constraint define 1 [Foreign]_Irep78_2 = 1

. constraint define 2 [Foreign]_Irep78_3 = 1

. constraint define 3 [Foreign]_Irep78_4 = 1

. constraint define 4 [Foreign]_Irep78_5 = 1

. mlogit foreign price gear _*  , c(1-4)

Iteration 0:   log likelihood = -49.584463
Iteration 1:   log likelihood = -21.248164
Iteration 2:   log likelihood = -17.091979
Iteration 3:   log likelihood = -16.212473
Iteration 4:   log likelihood = -16.137122
Iteration 5:   log likelihood = -16.136264
Iteration 6:   log likelihood = -16.136264

Multinomial logistic regression                   Number of obs   =         69
                                                  LR chi2(2)      =      66.90
                                                  Prob > chi2     =     0.0000
Log likelihood = -16.136264                       Pseudo R2       =     0.6746

 ( 1)  [Foreign]_Irep78_2 = 1
 ( 2)  [Foreign]_Irep78_3 = 1
 ( 3)  [Foreign]_Irep78_4 = 1
 ( 4)  [Foreign]_Irep78_5 = 1
------------------------------------------------------------------------------
     foreign |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Foreign      |
       price |   .0004787    .000196     2.44   0.015     .0000944    .0008629
  gear_ratio |   7.562929   1.845069     4.10   0.000      3.94666     11.1792
   _Irep78_2 |          1          .        .       .            .           .
   _Irep78_3 |          1          .        .       .            .           .
   _Irep78_4 |          1          .        .       .            .           .
   _Irep78_5 |          1          .        .       .            .           .
       _cons |   -28.2563   6.600582    -4.28   0.000    -41.19321    -15.3194
------------------------------------------------------------------------------
(Outcome foreign==Domestic is the comparison group)

.

Hope this helps,
Scott


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