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From |
"Martin Weiss" <martin.weiss1@gmx.de> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: AW: AW: GLM family and link (default) |

Date |
Mon, 14 Jun 2010 13:33:59 +0200 |

<> 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... HTH Martin -----Ursprüngliche Nachricht----- Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von mmolina@uniroma3.it Gesendet: Montag, 14. Juni 2010 13:16 An: statalist@hsphsun2.harvard.edu Betreff: st: AW: GLM family and link (default) 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" <martin.weiss1@gmx.de> To <statalist@hsphsun2.harvard.edu> Subject st: AW: GLM family and link (default) Date Mon, 14 Jun 2010 13:01:47 +0200 ---------------------------------------------------------------------------- ---- <> " 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: mmolina@uniroma3.it Data: Lun, 14 Giugno 2010 12:36 pm A: statalist@hsphsun2.harvard.edu -------------------------------------------------------------------------- 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. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: AW: GLM family and link (default)***From:*mmolina@uniroma3.it

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