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From |
Nick Winter <nwinter@virginia.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: probit vs. logit |

Date |
Tue, 25 May 2010 10:17:33 -0400 |

- Nick On 5/25/2010 2:55 AM, Michael N. Mitchell wrote:

I agree with Martin, that the choice of "logit" vs. "probit" appears to be largely discipline specific. If this is for publication or presentation, then it might be useful to see what the customs are for your audience. If someone gets picky with you and really wants to see a comparison of the model fit of the two models, I think you could use -estimates store- and -estimates stats- (as shown below) to compare the fit of the models using the AIC and/or BIC (where a smaller value means better fit). As in the example below, the two values are nearly identical, and I think we all expect that this would generally be the case. --- 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/

-- -------------------------------------------------------------- Nicholas Winter 434.924.6994 t Assistant Professor 434.924.3359 f Department of Politics nwinter@virginia.edu e University of Virginia faculty.virginia.edu/nwinter w S385 Gibson Hall, South Lawn * * 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:*Richard Williams <richardwilliams.ndu@gmail.com>

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

**Re: st: probit vs. logit***From:*"Michael N. Mitchell" <Michael.Norman.Mitchell@gmail.com>

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