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Re: st: Re: Fixing an -ml model- syntax problem


From   Richard Williams <Richard.A.Williams.5@ND.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Re: Fixing an -ml model- syntax problem
Date   Tue, 20 Feb 2007 15:37:16 -0500

At 12:55 PM 2/20/2007, Sergiy Radyakin wrote:
.    ml model lf gplogit (union = age black grade south) (delta: black,nocons)
. ml maximize
                                                 Number of obs   = 26200
                                                 Wald chi2(4)    = 1073.40
Log likelihood = -13267.224                       Prob > chi2     = 0.0000

------------------------------------------------------------------------------
      union |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
eq1          |
        age |   .0119869   .0023941     5.01   0.000     .0072945 .0166793
      black |   .8475739   .0615269    13.78   0.000     .7269834 .9681643
      grade |     .07586   .0069451    10.92   0.000     .0622479 .0894721
      south |  -.9233356   .0435432   -21.21 0.000    -1.008679   -.8379924
      _cons |   -2.52238   .1121859   -22.48 000    -2.742261     -2.3025
-------------+----------------------------------------------------------------
delta        |
      black |  -.0198168   .0680003    -0.29   0.771    -.1530949 .1134614
------------------------------------------------------------------------------
Here is how to get the same results in -oglm-. (oglm stands for Ordinal Generalized Linear Models, but such models may be better know by such names as location/scale models or heterogeneous choice models. oglm was inspired by SPSS's PLUM routine but has some Stata-ish features that PLUM does not.)

. webuse union
(NLS Women 14-24 in 1968)

. oglm union age black grade south, het(black)

Heteroskedastic Ordered Logistic Regression Number of obs = 26200
LR chi2(5) = 1194.01
Prob > chi2 = 0.0000
Log likelihood = -13267.224 Pseudo R2 = 0.0431

------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
union |
age | .0119869 .0023941 5.01 0.000 .0072945 .0166793
black | .8475699 .0615274 13.78 0.000 .7269783 .9681614
grade | .0758602 .0069451 10.92 0.000 .0622481 .0894723
south | -.9233374 .0435432 -21.21 0.000 -1.008681 -.8379942
-------------+----------------------------------------------------------------
lnsigma |
black | .0200207 .0693753 0.29 0.773 -.1159524 .1559938
-------------+----------------------------------------------------------------
/cut1 | 2.522383 .112186 22.48 0.000 2.302502 2.742263
------------------------------------------------------------------------------

. * Simple algebra converts log of sigma into Allison's delta
. scalar lnsigma = [lnsigma]_b[black]
. display "Allison's delta = " (1 - exp(lnsigma))/ exp(lnsigma)
Allison's delta = -.01982162

. * This reproduces the Wald test from Allison's procedure
. test [union]

( 1) [union]age = 0
( 2) [union]black = 0
( 3) [union]grade = 0
( 4) [union]south = 0

chi2( 4) = 1073.40
Prob > chi2 = 0.0000

oglm has assorted advantages over Allison's code. Among other things, it is not limited to a single dichotomous variable in the heteroskedasticity equation, it works with ordinal as well as dichotomous dependent variables, it allows link functions besides logit, and it lets you take advantage of various advanced Stata features, e.g. prefix commands like svy and nestreg.

-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
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