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st:glm with bin family and link probit VS. probit


From   Judy You <joodyu@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st:glm with bin family and link probit VS. probit
Date   Tue, 31 May 2011 13:43:11 +0930

Dear Stata Experts:



I have a question regards to comparisons of the two models: glm with
bin family and link probit VS. probit.



The data is number of people who died from infection disease by age
group as follows.



agegp  0          1



0          154573            2

15        97581  0

25        159888            9

40        191122            35

65        29329  20



I got the different results by using glm with bin family and link
probit and probit. The main difference is that glm dropped the last
dummy variable “f65”, while keep all the coefficient even with the zeo
values (eg., f15 and m15). The Probit dropped not only f65, but also
the zeo values eg., f15 and m15. The different results lead to
different estimation of marginal effects followed by the two models.
Any idea and advice how to control the two models using the same
independent dummy variables?



Your help will be much appreciated!



. glm  AB   m0- f65  [fw= ABfreq], f(b) l(probit) iterate(10)

note: f65 omitted because of collinearity



Iteration 0:   log likelihood =  -47450.19

Iteration 1:   log likelihood = -825.64878

Iteration 2:   log likelihood = -629.32298

Iteration 3:   log likelihood =  -622.2711

Iteration 4:   log likelihood = -621.33495

Iteration 5:   log likelihood = -621.31077

Iteration 6:   log likelihood = -621.30724

Iteration 7:   log likelihood = -621.30711

Iteration 8:   log likelihood = -621.30711

Iteration 9:   log likelihood = -621.30711

Iteration 10:  log likelihood = -621.30711

convergence not achieved



Generalized linear models                          No. of obs      =   632559

Optimization     : ML                              Residual df     =
     632549

Scale parameter =      1

Deviance         =  1242.614219                    (1/df) Deviance =
        .0019645

Pearson          =   534978.001                    (1/df) Pearson  = .8457495



Variance function: V(u) = u*(1-u)                  [Bernoulli]

Link function    : g(u) = invnorm(u)               [Probit]



AIC             =   .001996

Log likelihood   = -621.3071096                    BIC             =
  -8448049





OIM

AB       Coef.   Std. Err.      z    P>z     [95% Conf.     Interval]



m0   -.9250873   .2495697    -3.71   0.000    -1.414235         -.4359398

m15   -2.901722   7.830577    -0.37   0.711    -18.24937       12.44593

m25   -.5044691    .146919    -3.43   0.001     -.792425         -.2165132

m40   -.2180508   .1199777    -1.82   0.069    -.4532027       .0171012

m65    .1468668    .133816     1.10   0.272    -.1154077         .4091414

f0   -.9122453   .2501379    -3.65   0.000    -1.402507           -.4219841

f15   -2.901722   8.073848    -0.36   0.719    -18.72617         12.92273

f25   -.6696904   .1741904    -3.84   0.000    -1.011097         -.3282836

f40   -.3572294   .1297122    -2.75   0.006    -.6114606         -.1029981

f65   (omitted)

_cons   -3.288246   .1063749   -30.91   0.000    -3.496737   -3.079755



. probit  AB   m0- f65  [fw= ABfreq], iterate(10)



note: m15 != 0 predicts failure perfectly

m15 dropped and 190 obs not used



note: f15 != 0 predicts failure perfectly

f15 dropped and 190 obs not used



note: f65 omitted because of collinearity

Iteration 0:   log likelihood = -660.01746

Iteration 1:   log likelihood =  -629.8237

Iteration 2:   log likelihood =  -621.8218

Iteration 3:   log likelihood = -621.31032

Iteration 4:   log likelihood = -621.30614

Iteration 5:   log likelihood = -621.30613



Probit regression                                 Number of obs   =      534978

LR chi2(7)      =           77.42

Prob > chi2     =          0.0000

Log likelihood = -621.30613                       Pseudo R2       = 0.0587





AB       Coef.   Std. Err.      z    P>z     [95% Conf.     Interval]



m0   -.9250871   .2495696    -3.71   0.000    -1.414235         -.4359397

m15   (omitted)

m25   -.5044691    .146919    -3.43   0.001     -.792425         -.2165132

m40   -.2180508   .1199777    -1.82   0.069    -.4532027       .0171012

m65    .1468668    .133816     1.10   0.272    -.1154077         .4091414

f0   -.9122452   .2501378    -3.65   0.000    -1.402506           -.4219841

f15   (omitted)

f25   -.6696904   .1741904    -3.84   0.000    -1.011097         -.3282836

f40   -.3572294   .1297122    -2.75   0.006    -.6114606         -.1029981

f65   (omitted)

_cons   -3.288246   .1063749   -30.91   0.000    -3.496737   -3.079755

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