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You are trying to estimates >10 parameters from 33 observations. That is a
problem no amount of wizardry in terms of different commands will be able to
overcome...
-------------------------- Messaggio originale ---------------------------
Oggetto: st: AW: GLM family and link (default)
Da: [email protected]
Data: Lun, 14 Giugno 2010 1:16 pm
A: [email protected]
--------------------------------------------------------------------------
Thanks Martin.
I obtain these results with glm and probit commands:
glm newproc edu train skilled quality RD n5a rectech
d12b obst_admin tax j30f environ if a4b==1
Iteration 00:00 log likelihood = -2.7185812
Generalized linear models No. of obs = 33
Optimization : ML Residual df = 20
Scale parameter = 0.1139108
Deviance = 2.278216706 (1/df) Deviance = 0.1139108
Pearson = 2.278216706 (1/df) Pearson = 0.1139108
Variance function: V(u) = 1 [Gaussian]
Link function : g(u) = u [Identity]
AIC = 0.9526413
Log likelihood = -2.718581171 BIC = -67.65193
OIM
newproc Coef. Std. Err. z P>z [95% Conf. Interval]
edu -1.770208 1.005382 -1.76 0.078 -3.740721 0.2003048
train 0.4414615 0.1819173 2.43 0.015 0.08491 0.7980129
skilled -0.0943376 0.2310215 -0.41 0.683 -0.5471315 0.3584562
quality 0.1508587 0.1473651 1.02 0.306 -0.1379717 0.4396891
RD -8.58E-11 3.80E-10 -0.23 0.821 -8.31E-10 6.59E-10
n5a 8.95E-13 8.61E-13 1.04 0.299 -7.92E-13 2.58E-12
rectech 0.1898761 0.2048377 0.93 0.354 -0.2115985 0.5913507
d12b -0.0024934 0.0028224 -0.88 0.377 -0.0080251 0.0030384
obst_admin 0.001645 0.0019013 0.87 0.387 -0.0020814 0.0053714
tax 0.0093363 0.0395494 0.24 0.813 -0.068179 0.0868516
j30f -0.0119285 0.0366119 -0.33 0.745 -0.0836865 0.0598294
environ -0.1940408 0.0896125 -2.17 0.03 -0.369678 -0.0184036
_cons 1.822709 0.8467213 2.15 0.031 0.163166 3.482252
probit newproc edu train skilled quality RD n5a rectech
d12b obst_admin tax j30f environ if a4b==1
note: outcome = edu < 0.75 predicts data perfectly except for
edu == 0.75 subsample:
edu dropped and 4 obs not used
note: rectech != 0 predicts success perfectly
rectech dropped and 7 obs not used
Iteration 00:00 log likelihood = -11.791118
Iteration 01:00 log likelihood = -3.7934494
Iteration 02:00 log likelihood = -2.2015708
Iteration 03:00 log likelihood = -1.2485201
Iteration 04:00 log likelihood = -0.40922628
Iteration 05:00 log likelihood = -0.17252348
Iteration 06:00 log likelihood = -0.0510138
Iteration 07:00 log likelihood = -0.01540391
Iteration 08:00 log likelihood = -0.00477621
Iteration 09:00 log likelihood = -0.00153132
Iteration 10:00 log likelihood = -0.00050221
Iteration 11:00 log likelihood = -0.00016739
Iteration 12:00 log likelihood = -0.00005647
Iteration 13:00 log likelihood = -0.00001923
Iteration 14:00 log likelihood = -6.60E-06
Iteration 15:00 log likelihood = -2.28E-06
Iteration 16:00 log likelihood = -7.90E-07
Iteration 17:00 log likelihood = -2.76E-07
Iteration 18:00 log likelihood = -2.19E-07
Iteration 19:00 log likelihood = -7.50E-08
Iteration 20:00 log likelihood = -7.43E-08
Iteration 21:00 log likelihood = -2.40E-08
Iteration 22:00 log likelihood = -2.33E-08
Iteration 23:00 log likelihood = -2.33E-08 (backed up)
Iteration 24:00:00 log likelihood = -2.42E-08 (backed up)
Probit regression Number of obs = 22
LR chi2(10) = 23.58
Prob > chi2 = 0.0088
Log likelihood = -2.42E-08 Pseudo R2 = 1
newproc Coef. Std. Err. z P>z [95% Conf. Interval]
train 35.10353 . . . . .
skilled -31.46968 . . . . .
quality -5.014139 . . . . .
RD -2.77E-08 0.0001747 0 1 -0.0003424 0.0003424
n5a -1.37E-08 8.75E-06 0 0.999 -0.0000172 0.0000171
d12b -0.3628383 106.9626 0 0.997 -210.0057 209.28
obst_admin 1.958071 1313.844 0 0.999 -2573.129 2577.045
tax 2.494215 . . . . .
j30f 11.64055 2769.635 0 0.997 -5416.744 5440.026
environ -10.03463 12953.29 0 0.999 -25398.02 25377.95
_cons 2.51716 7373.722 0 1 -14449.71 14454.75
Note: 3 failures and 12 successes completely determined.
Martin Weiss" <[email protected]>
To <[email protected]>
Subject st: AW: GLM family and link (default)
Date Mon, 14 Jun 2010 13:01:47 +0200
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<>
" Actually
this seems to work better than the probit command."
"Work better" is not an expression that conveys much to me. In which respect
did it work better?
Note you can replicate the linear probability model, -probit- and -logit-
via -glm-:
*************
sysuse auto, clear
reg foreign length weight
glm foreign length weight, family(gaussian) link(identity) nolog
prob foreign length weight, nolog
glm foreign length weight, family(binomial 1) link(probit) nolog
logit foreign length weight, nolog
glm foreign length weight, family(binomial 1) link(logit) nolog
*************
-------------------------- Messaggio originale ---------------------------
Oggetto: GLM family and link (default)
Da: [email protected]
Data: Lun, 14 Giugno 2010 12:36 pm
A: [email protected]
--------------------------------------------------------------------------
Dear Statlist,
Looking at the glm help I found that the distribution of the dependent
variable -by default- is family(gaussian).
I am working with glm command, I did not specify any specific type of
family or link function, and I have a binary dependent variable. Actually
this seems to work better than the probit command.
As I don't have continuous Gaussian responses but binary ones, which
should be the distribution family and link function underlying this
command?
Thanks in advance.
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