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st:glm with bin family and link probit VS. probit
From
Judy You <[email protected]>
To
[email protected]
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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