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From |
Gao Liu <gao.liu@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: pseudo R-squared |

Date |
Sun, 3 Apr 2011 21:46:52 -0600 |

Thanks, Richard, for your detail explanation. That's very helpful. One more question: how to calculate the count R-squared (#correct/total obs.) of partial proportional ordered logistic regression. I know how to calculate the count R-squared of logistic or ordered logistic regressions, but the calculation does not seem to work for partial proportional ordered logistic regression. Thank you very much. Gao On Sun, Apr 3, 2011 at 4:48 PM, Richard Williams <richardwilliams.ndu@gmail.com> wrote: > At 04:15 PM 4/3/2011, Gao Liu wrote: >> >> Thanks, Nick. Right, it should be gologit2. So it is McFadden's Pseudo >> R^2. >> >> Can I simply use the difference of the pseudo's R^2 of model with the >> focal variable and that without to explain the increased explanatory >> power? pseudo-R2 (with the variable)-pseudo-R2 (without the >> variable)=(L1(with)-L1(without))/L0. Can I use this result as the >> increased explanatory power? Are there any other ways to answer the >> question of "how much more likely is the predictor able to predict the >> change of categorical dependent variable" >> >> Thanks very much for your explanation. > > First off, thanks to Nick for answering the first part of the Q. gologit2 > uses the McFadden's Pseudo R^2, just like logit and ologit and assorted > other Stata routines do. If somebody had a burning desire to calculate one > of the other Pseudo R^2 measures it probably would not be too hard to do so. > > With regards to the 2nd part of the question, I think the most customary > thing to do is either a Wald test of the variable in question or a LR > chi-square contrast between the model that has the variable and the model > that doesn't. So, if the variable of interest was education, you could do > something like > > use "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";, clear > gologit2 warm yr89 male white age prst ed > test ed > nestreg, lr: gologit2 warm (yr89 male white age prst) (ed) > > When contrasting models, just make sure they really are nested, e.g. you > don't want to be imposing different proportionality constraints in your > different models while at the same time dropping or adding variables. > > > ------------------------------------------- > Richard Williams, Notre Dame Dept of Sociology > OFFICE: (574)631-6668, (574)631-6463 > HOME: (574)289-5227 > EMAIL: Richard.A.Williams.5@ND.Edu > WWW: http://www.nd.edu/~rwilliam > > * > * 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/ > -- Gao Liu, PhD Assistant Professor in Public Administration University of New Mexico Phone: 505-277-0418 Fax: 505-277-2529 gliu@unm.edu * * 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: pseudo R-squared***From:*Richard Williams <richardwilliams.ndu@gmail.com>

**References**:**st: pseudo R-squared***From:*Gao Liu <gao.liu@gmail.com>

**Re: st: pseudo R-squared***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: pseudo R-squared***From:*Gao Liu <gao.liu@gmail.com>

**Re: st: pseudo R-squared***From:*Richard Williams <richardwilliams.ndu@gmail.com>

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