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From |
"Michael N. Mitchell" <Michael.Norman.Mitchell@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: probit vs. logit |

Date |
Mon, 24 May 2010 23:55:30 -0700 |

--- snip --- . sysuse auto (1978 Automobile Data) . logit foreign mpg price weight Iteration 0: log likelihood = -45.03321 Iteration 1: log likelihood = -22.244792 Iteration 2: log likelihood = -18.069284 Iteration 3: log likelihood = -17.184699 Iteration 4: log likelihood = -17.161975 Iteration 5: log likelihood = -17.161893 Iteration 6: log likelihood = -17.161893 Logistic regression Number of obs = 74 LR chi2(3) = 55.74 Prob > chi2 = 0.0000 Log likelihood = -17.161893 Pseudo R2 = 0.6189 ------------------------------------------------------------------------------ foreign | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- mpg | -.1210918 .0956855 -1.27 0.206 -.308632 .0664483 price | .0009264 .0003074 3.01 0.003 .000324 .0015288 weight | -.0068497 .0019996 -3.43 0.001 -.0107688 -.0029306 _cons | 14.42237 5.414367 2.66 0.008 3.81041 25.03434 ------------------------------------------------------------------------------ . estimates store model1 . probit foreign mpg price weight teration 0: log likelihood = -45.03321 Iteration 1: log likelihood = -20.083125 Iteration 2: log likelihood = -17.363271 Iteration 3: log likelihood = -17.152935 Iteration 4: log likelihood = -17.151715 Iteration 5: log likelihood = -17.151715 Probit regression Number of obs = 74 LR chi2(3) = 55.76 Prob > chi2 = 0.0000 Log likelihood = -17.151715 Pseudo R2 = 0.6191 ------------------------------------------------------------------------------ foreign | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- mpg | -.0723615 .0556501 -1.30 0.193 -.1814337 .0367106 price | .0005185 .0001651 3.14 0.002 .000195 .0008421 weight | -.0038232 .0010392 -3.68 0.000 -.00586 -.0017864 _cons | 8.150001 2.962982 2.75 0.006 2.342664 13.95734 ------------------------------------------------------------------------------ . estimates store model2 . estimates stats model1 model2 ----------------------------------------------------------------------------- Model | Obs ll(null) ll(model) df AIC BIC -------------+--------------------------------------------------------------- model1 | 74 -45.03321 -17.16189 4 42.32379 51.54005 model2 | 74 -45.03321 -17.15171 4 42.30343 51.51969 ----------------------------------------------------------------------------- Note: N=Obs used in calculating BIC; see [R] BIC note --- snip ---- I hope that helps, Michael N. Mitchell Data Management Using Stata - http://www.stata.com/bookstore/dmus.html A Visual Guide to Stata Graphics - http://www.stata.com/bookstore/vgsg.html Stata tidbit of the week - http://www.MichaelNormanMitchell.com On 2010-05-24 11.36 PM, Maarten buis wrote:

--- On Mon, 24/5/10, SR Millis wrote:Logistic regression is generally preferred over the probit model because of the wider variety of fit statistics. Also, exponentiated logit coefficients can be interpreted as odds ratios---which is not the case with probit coefficients.A general preference for one or the other is to a large extend discipline dependent. For example, within economics the probit is the "default" method. I like interpreting effects in terms of odds ratios as a way of identifying the scale, which is unidentified in a probit model (it is identified by fixing the residual variance to one, which has all kinds of nasty consequences when interpreting interaction terms). So, I tend to use the -logit-. -- Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * 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/

**Follow-Ups**:**Re: st: probit vs. logit***From:*Nick Winter <nwinter@virginia.edu>

**Re: st: probit vs. logit***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**RE: st: probit vs. logit***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**References**:**Re: st: probit vs. logit***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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