[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
marcel spijkerman <marcel_spijkerman@hotmail.com> |

To |
stata stata <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Re: computation of R-squared with a non-linear model |

Date |
Fri, 22 May 2009 09:02:49 +0000 |

Instead of a linear regression I use a weighted nl-procedure using transformed (square-roots) variabels. Indeed, both the adjusted and unadjusted R-squared are 1.000. Actually, what I finally did was what Paul suggests below. First I use predict after estimating the model, then I transform the predicted values back to their original state and compute correlations between the original dependent and predicted values, weigthed by the same weight I use in the regression. The adjusted R-squared is not 1.000 but 0.96. Still very high of course but that has to do with the model. However, I followed up Maarten's suggestion to experiment with different starting values. No matter what starting values I use, the model converges to the same results. I estimated the same model in E-views and results are exactly the same. So, I don't think there is something wrong with convergence. Instead, I think that Stata returns 1.000 because of the transformed variabels. The R-square actually is not really 1.000 but something like 0.999999. So, I guess that things are solved now. Thanks to everyone who answered. Marcel > Date: Fri, 22 May 2009 09:25:00 +0100 > From: paul.seed@kcl.ac.uk > To: statalist@hsphsun2.harvard.edu > Subject: st: Re: computation of R-squared with a non-linear model > > There is a simple way to compute R-squared for any regression model, > if you do not believe the value given by Stata: Calculate the predicted > values and carry out your own correlation. > > Using the auto data set: > > **** Start Stata code ***** > sysuse auto > regress weight price > > predict pred_w > su weight pred_w > corr weight pred_w > > di "R-squared = " r(rho)*r(rho) > **** End Stata code ***** > > Both ways giver a value of 0.2901023 > In general, the use of weights and adjusted R-squared > makes things more complicated, and the last two lines > could be changed to allow for them; > but neither will alter a correltion of 1.0. > > If Marcel Spijkerman uses this approach, he may find > a) Marcel is right - the second R-squared is different > from the first. (He does not say, but I assume that > both the adjusted and unadjusted R-squared are 1.0). > > b) Martin Buis is right - the model has failed to converge, > and the predicted values are mostly or completely undefined. > > c) Stata is right - both methods give R-squared = 1.0 > > d) Something else I haven't though to. > > I'd be interested to know which. > > > > >>> Date: Wed, 20 May 2009 08:27:31 +0000 >>> From: maartenbuis@yahoo.co.uk >>> Subject: RE: st: Re: computation of R-squared with a non-linear model >>> To: statalist@hsphsun2.harvard.edu >>> >>> >>> --- "marcel spijkerman" wrote: >> >>>>>>>>> I estimate a weighted non-linear model of the >>>>>>>>> following form: >>>>>>>>> >>>>>>>>> y0.5 = (a1 + a2*X)0.5 weighted by some other >>>>>>>>> variable z. >>>>>>>>> >>>>>>>>> Stata reports an adjusted R-squared of 1.000. I >>>>>>>>> suspect this is not correct. How can compute the >>>>>>>>> correct adjusted R-squared using untransformed >>>>>>>>> variables? >>>>> >>> >>> --- Martin Weiss wrote: >> >>>>>>> Which command did you use for your estimation? If it >>>>>>> was non-linear, -nl-? >>>>>>> Show us what you typed and what the reply was... >>>> >>> >>> --- On Wed, 20/5/09, marcel spijkerman wrote: >> >>>>> I indeed typed the command: >>>>> >>>>> nl (sqrt_y = (a1 + a2X)0.5) [aweight= hhd1564_06] >>>>> >>>>> And the answer is .... :-) >>> >>> >>> It is surprising that you got output at all, as you >>> did not specify any parameters. Anyhow, it appears that >>> you have a convergence problem. So the next step is >>> not to try to come up with some correct R2, but to >>> fix the model. You could try specifing starting >>> values. >>> >>> -- Maarten >>> >>> Ps. -aweights- are very very very rarely the correct >>> weight type. >>> > Paul T Seed MSc CStat CSci, Senior Lecturer in Medical Statistics, > tel (+44) (0) 20 7188 3642, fax (+44) (0) 20 7620 1227 > Wednesdays: (+44) (0) 20 7848 4208 > > > > * > * 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/ _________________________________________________________________ Express yourself instantly with MSN Messenger! Download today it's FREE! http://messenger.msn.click-url.com/go/onm00200471ave/direct/01/ * * 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/

**References**:**st: Re: computation of R-squared with a non-linear model***From:*Paul Seed <paul.seed@kcl.ac.uk>

- Prev by Date:
**st: Re: computation of R-squared with a non-linear model** - Next by Date:
**Re: st: Survival between groups** - Previous by thread:
**st: Re: computation of R-squared with a non-linear model** - Next by thread:
**st: RE: Re: computation of R-squared with a non-linear model** - Index(es):

© Copyright 1996–2014 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |