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

["Joao Ricardo F. Lima" <jricardofl@gmail.com>]

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.
>> *
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>>
>
>
>
> --
> ----------------------------------------
> Joao Ricardo Lima, D.Sc.
> Professor
> UFPB-CCA-DCFS
> Fone: +558387264913
> Skype: joao_ricardo_lima
> ----------------------------------------
>
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-- 
----------------------------------------
Joao Ricardo Lima, D.Sc.
Professor
UFPB-CCA-DCFS
Fone: +558387264913
Skype: joao_ricardo_lima
----------------------------------------

*
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