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From |
"Joao Ricardo F. Lima" <jricardofl@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Comparison of the R-squared in a loglog and linear model |

Date |
Sat, 19 Jun 2010 17:46:51 -0300 |

Martin, exactly! Thx! JL 2010/6/19 Martin Weiss <martin.weiss1@gmx.de>: > > <> > > Shorthand for a "million", I would say: > > ************* > di 1e6 > ************* > > > > HTH > Martin > > > -----Ursprüngliche Nachricht----- > Von: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Joao Ricardo F. > Lima > Gesendet: Samstag, 19. Juni 2010 19:11 > An: statalist@hsphsun2.harvard.edu > Betreff: Re: st: Comparison of the R-squared in a loglog and linear model > > Austin, > > the question is not my opinion to the thread. I only don't understand > this part of the code: > > g mse_xb=(totexp-xb)^2/1e6 > > What's -1e6-?? > > Thx a lot, > > Joao Lima > > 2010/6/18 Austin Nichols <austinnichols@gmail.com>: >> Kit et al.-- >> Duan's smearing method is one approach to dealing with a logged >> depvar; a better approach is to use a regression technique that >> respects the functional form, like -poisson- (or another member of the >> -glm- family). But you still cannot compare the R-squared across >> non-nested models and hope to conclude anything about which model is >> better from that information alone. Mean squared prediction error in >> levels for the nonzero outcomes seems a reasonable criterion for >> rejecting the log(y) regression model below. >> >> use http://fmwww.bc.edu/ec-p/data/mus/mus03data, clear >> qui reg totexp suppins phylim actlim totchr age female income >> predict xb >> qui reg ltotexp suppins phylim actlim totchr age female income >> levpredict tenorm >> levpredict teduan, duan print >> qui poisson totexp suppins phylim actlim totchr age female income >> predict tepois >> qui nbreg totexp suppins phylim actlim totchr age female income >> predict tenbreg >> su totexp xb te* >> su totexp xb te* if totexp>0 >> corr totexp xb te* >> >> g mse_norm=(totexp-tenorm)^2/1e6 >> g mse_duan=(totexp-teduan)^2/1e6 >> g mse_pois=(totexp-tepois)^2/1e6 >> g mse_nbreg=(totexp-tenbreg)^2/1e6 >> su mse* >> su mse* if totexp>0 >> >> Variable | Obs Mean Std. Dev. Min Max >> -------------+-------------------------------------------------------- >> mse_xb | 2955 127.0504 642.6503 .00005 12779.11 >> mse_norm | 2955 142.4353 641.0374 3.32e-06 11744.09 >> mse_duan | 2955 140.7604 644.1605 .0000549 11842.16 >> mse_pois | 2955 128.3255 648.1356 4.52e-06 12841.78 >> mse_nbreg | 2955 131.8694 642.3027 2.48e-06 12432.65 >> >> For those enamored of scatter plots for this kind of comparison, much >> more work is required to get a good picture of fit. This is one >> approach: >> >> g cr_te=totexp^(1/3) >> g cr_xb=sign(xb)*abs(xb)^(1/3) >> g cr_norm=tenorm^(1/3) >> g cr_duan=teduan^(1/3) >> g cr_pois=tepois^(1/3) >> g cr_nbreg=tenbreg^(1/3) >> sc cr_* cr_te if totexp>0, msize(1 1 1 1 1 1) >> >> On Fri, Jun 18, 2010 at 9:47 AM, Christopher Baum <kit.baum@bc.edu> wrote: >>> <> >>> On Jun 18, 2010, at 2:33 AM, Natalie wrote: >>> >>>> Can I not maybe obtain the antilog predicted values for the log log >>>> model and compute the R-squared between the antilog of the observed and >>>> predicted values. And then compare this R-square with the R-square >>>> obtained from OLS estimation of the linear model? >>>> >>>> There are other statistical programs that can do this automatically, but >>>> as I work with Stata, I'd rather do it with this program. >>> >>> >>> findit levpredict >>> >>> Generate the level form of the dependent variable (correctly, using this > routine) and then >>> compute the squared correlation between that and the original level > variable. That will be the >>> R^2 of the log form of the regression. >> * >> * 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/ >> > > > > -- > ---------------------------------------- > Joao Ricardo Lima, D.Sc. > Professor > UFPB-CCA-DCFS > Fone: +558387264913 > Skype: joao_ricardo_lima > ---------------------------------------- > > * > * 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/ > -- ---------------------------------------- Joao Ricardo Lima, D.Sc. Professor UFPB-CCA-DCFS Fone: +558387264913 Skype: joao_ricardo_lima ---------------------------------------- * * 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**:**Re: st: Comparison of the R-squared in a loglog and linear model***From:*Christopher Baum <kit.baum@bc.edu>

**Re: st: Comparison of the R-squared in a loglog and linear model***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: Comparison of the R-squared in a loglog and linear model***From:*"Joao Ricardo F. Lima" <jricardofl@gmail.com>

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