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From |
Joerg Luedicke <joerg.luedicke@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Interpretation of Cut Points in Ordered Probit (Logit) Model |

Date |
Fri, 26 Apr 2013 12:48:57 -0400 |

The thresholds, or cut points, reflect the predicted cumulative probabilities at covariate values of zero. Consider the following simple example for an ordered logit model with one binary predictor variable. Have a look at the predicted probabilities from -margins- (and calculate the cumulative probability for y==2) and compare them with the predicted cumulative probabilities that are calculated using the thresholds: *------------------- * toy data clear sysuse auto * create outcome with 3 categories gen y = rep78 - 2 if rep78 > 2 * fit ordered logit model ologit y i.foreign * predict probabilities margins i.foreign, predict(outcome(1)) margins i.foreign, predict(outcome(2)) * cumulative probability for y == 2 di .7098536 + .2397166 * calculate predicted cumulative probabilities for foreign == 0 from cutpoints di exp(_b[cut1:_cons]) / (1 + exp(_b[cut1:_cons])) di exp(_b[cut2:_cons]) / (1 + exp(_b[cut2:_cons])) *------------------- In principle, this is similar to an ordered probit model, you just would plug the thresholds into the cumulative standard normal distribution instead of using the inverse logit transform. Joerg On Fri, Apr 26, 2013 at 10:11 AM, Auh, Jun Kyung <jauh14@gsb.columbia.edu> wrote: > Thanks. So I need to add back mean of X to generate predicted Y (not Pr(Y=i)) based on the cut points, right? > Just want to make sure that I understood. > > Best, > JK > > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of JVerkuilen (Gmail) > Sent: Thursday, April 25, 2013 11:40 PM > To: statalist@hsphsun2.harvard.edu > Subject: Re: st: Interpretation of Cut Points in Ordered Probit (Logit) Model > > On Thu, Apr 25, 2013 at 9:25 PM, Auh, Jun Kyung <jauh14@gsb.columbia.edu> wrote: >> >> The regression results show that I have negative value for cut2. >> If cut2 < 0, wouldn't the model predict that there is zero observation with Y = 1? >> Or, is the cut points are de-meaned? i.e, do I have to add back the mean of X to have correct interpretation? > > Like intercept type parameters in other models, the cuts are relative to the mean of X. > > > -- > JVVerkuilen, PhD > jvverkuilen@gmail.com > > "He uses statistics as a drunken man uses lamp-posts - for support rather than illumination."--Andrew Lang > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/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/faqs/resources/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/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Interpretation of Cut Points in Ordered Probit (Logit) Model***From:*"Auh, Jun Kyung" <jauh14@gsb.columbia.edu>

**Re: st: Interpretation of Cut Points in Ordered Probit (Logit) Model***From:*"JVerkuilen (Gmail)" <jvverkuilen@gmail.com>

**RE: st: Interpretation of Cut Points in Ordered Probit (Logit) Model***From:*"Auh, Jun Kyung" <jauh14@gsb.columbia.edu>

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