[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
yumin sheng <shengyumin@yahoo.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
RE: st: plot predicted effects after regression |

Date |
Sun, 9 May 2004 12:02:41 -0700 (PDT) |

Dear Nick, Richard and Tom, Thanks so much to you all again. I might have been wrong in believing that Stata has a ready command; the "graph twoway" series seem to work only for 1 X variable. Best, yumin --- Nick Cox <n.j.cox@durham.ac.uk> wrote: > I would be surprised if that were true. > > However, it seems to me that the challenge is > not in the graphing; it is in the calculation. > > You can use -adjust-: you just need to talk > your way past the requirement for a -by()- > option (unless that is part of what you want). > > Here is a silly example: > > . sysuse auto > . regress mpg weight headroom turn trunk length > displacement > . gen all = 1 > . adjust headroom turn trunk length displacement, > by(all) gen(predict) > . scatter predict weight > > yumin sheng > > > Thanks so much. Your solution is great, but if I > > remember correctly, I think Stata has a ready and > very > > simple command for post-estimation graphing of the > > predicted effects. > > Thomas Trikalinos > > > > So you need predicted values on the > > > VariableOfInterest adjusting at the > > > mean level of continuous covariates, and the > > > reference category of > > > categoric covariates. > > > > > > A simple but not so elegant solution is > > > > > > . gen PredY = Constant + beta1* > VariableOfInterest1 > > > + > > > beta2*MeanContinuousCovariate2 (or the > > > corresponding analogue for a > > > logit/probit/poisson etc regression) > > > > > > [first run . egen MeanContinuousCovariate2 = > > > mean(ContinuousCovariate2)] > > > > > > Constant and beta1, beta2 are from the > regression > > > output. All > > > Categorical covariate terms are zero (this would > be > > > your reference > > > category, right?) and all the continuous > covariate > > > terms are > > > incorporated using their mean level. This way > you > > > adjust for the > > > reference category for categoric covariates and > for > > > the mean value of > > > continuous covariates. > > > > > > You most probably have more than one continuous > > > covariates; just put in > > > as many terms as you need. If you have different > > > functions of the > > > VariableOfInterest (eg quadratic or cubic terms) > put > > > them in as more > > > VariablesOfInterest. > > > > > > This is a crude workaround I use. I'm confident > that > > > people know > > > something better and more elegant, though... > > * > * For searches and help try: > * > http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ __________________________________ Do you Yahoo!? Win a $20,000 Career Makeover at Yahoo! HotJobs http://hotjobs.sweepstakes.yahoo.com/careermakeover * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**RE: st: plot predicted effects after regression***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

- Prev by Date:
**Re: st: forvalues question** - Next by Date:
**RE: st: plot predicted effects after regression** - Previous by thread:
**RE: st: plot predicted effects after regression** - Next by thread:
**RE: st: plot predicted effects after regression** - Index(es):

© Copyright 1996–2017 StataCorp LLC | Terms of use | Privacy | Contact us | What's new | Site index |