R help - Mar 2015 - measure of goodness of fit for the model without an intercept

Hi,

When I fit the regression model without an intercept term, R-squared tends
to much larger than the R-squared in the model with an intercept. So in this
case, what?s a more reasonable measure of the goodness of fit for the model
without an intercept?

Thanks a lot!! 



Yan 




	[[alternative HTML version deleted]]

On Thu, Jan 29, 2015 at 6:41 PM, Yan Wu <yanwu1205 at gmail.com>
wrote:
> Hi,
>
> When I fit the regression model without an intercept term, R-squared tends
> to much larger than the R-squared in the model with an intercept. So in
this
> case, what?s a more reasonable measure of the goodness of fit for the model
> without an intercept?
>
> Thanks a lot!!
>
> Yan
>
I am going through the list archives and found your question. I guess
it is unanswered because it is not directly related to R language per
se but is more to do with time series analysis in general.

In general, R square tells you only part of the story. You need to
look at the t-stats of the regression coefficients to understand
whether the betas from the regression are statistically significant.

Further, IIRC R square always increases as more variables are added to
the regression. That is why practitioners look at "adjusted r-square"
instead of "r square" which account for this. So I am curious as to
why your data produces less r square when you add the constant. Is it
possible to upload your data somewhere so pther can take a look at it?

thanks
-- 
Kamaraju S Kusumanchi | http://raju.shoutwiki.com/wiki/Blog

Hi

see Faq 7.41 Why does summary() report strange results for the R^2 estimate when
I fit a linear model with no intercept?

And some explanation in

http://stackoverflow.com/questions/20333600/why-does-summary-overestimate-the-r-squared-with-a-no-intercept-model-formula

I found somewhere
https://online.stat.psu.edu/~ajw13/stat501/SpecialTopics/Reg_thru_origin.pdf
that you can use correlation between observed and predicted values.

Quote:
Given these inconsistencies, Hocking (1996, p. 178)
notes: ?It is natural to ask if there is a measure
analogous to R2 for the no-intercept model. We
suggest the square of the sample correlation
between observed and predicted values?.

But I am not an expert in this matter.

Cheers
Petr
> -----Original Message-----
> From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of
> kamaraju kusumanchi
> Sent: Sunday, March 15, 2015 12:09 AM
> To: Yan Wu
> Cc: r-help at r-project.org
> Subject: Re: [R] measure of goodness of fit for the model without an
> intercept
>
> On Thu, Jan 29, 2015 at 6:41 PM, Yan Wu <yanwu1205 at gmail.com>
wrote:
> > Hi,
> >
> > When I fit the regression model without an intercept term, R-squared
> > tends to much larger than the R-squared in the model with an
> > intercept. So in this case, what?s a more reasonable measure of the
> > goodness of fit for the model without an intercept?
> >
> > Thanks a lot!!
> >
> > Yan
> >
>
> I am going through the list archives and found your question. I guess
> it is unanswered because it is not directly related to R language per
> se but is more to do with time series analysis in general.
>
> In general, R square tells you only part of the story. You need to look
> at the t-stats of the regression coefficients to understand whether the
> betas from the regression are statistically significant.
>
> Further, IIRC R square always increases as more variables are added to
> the regression. That is why practitioners look at "adjusted
r-square"
> instead of "r square" which account for this. So I am curious as
to why
> your data produces less r square when you add the constant. Is it
> possible to upload your data somewhere so pther can take a look at it?
>
> thanks
> --
> Kamaraju S Kusumanchi | http://raju.shoutwiki.com/wiki/Blog
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
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> PLEASE do read the posting guide http://www.R-project.org/posting-
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