From: Hofert Marius <m_hofert_at_web.de>

Date: Thu, 24 Apr 2008 07:57:53 +0200

R-help_at_r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Thu 24 Apr 2008 - 06:01:11 GMT

Date: Thu, 24 Apr 2008 07:57:53 +0200

Dear R-users,

I used lm() to fit a standard linear regression model to a given data
set, which led to a coefficient of determination (R^2) of about
0.96. After checking the residuals I realized that they follow an
autoregressive process (AR) of order 1 (and therefore contradicting
the i.i.d. assumption of the regression model). I then used gls()
[library nlme] to fit a linear regression model with AR(1)-residuals.
The residuals look perfect (residual plot, ACF, PACF, QQPlot, Ljung-
Box test).

As mentioned on http://en.wikipedia.org/wiki/
Coefficient_of_determination (citation [2008-04-24]: "For cases other
than fitting by ordinary least squares, the R^2 statistic can be
calculated as above" and later: "Values for R^2 can be calculated for
any type of predictive model"), I tried to calculate the standard R^2
for the model with AR(1) residuals. However, I ended up with R^2
larger than 1!

As mentioned on the German wikipedia page (http://de.wikipedia.org/
wiki/Bestimmtheitsmaß), in models fitted using Maximum Likelihood
Estimation (MLE), the coefficient of determination does _not_ exist
(citation [2008-04-24]: "Bei bestimmten statistischen Modellen, z.B.
bei Maximum-Likelihood-Schätzungen, existiert das Bestimmtheitsmaß
R^2 nicht"). Any comments on that?

The German Wikipedia page mentions McFadden's pseudo-coefficient of determination, the English Wikipedia page the one of Nagelkerke. I know there are others, too. Is there a general agreement on which "coefficient of determination" (or goodness-of-fit measure in general) to use for a regression model with autocorrelated errors? Is there a possibility to compare (non-graphically) the standard regression model with the model with AR(1) residuals to justify the better fit of the latter?

Any comments are appreciated.

Best regards.

Marius

R-help_at_r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Thu 24 Apr 2008 - 06:01:11 GMT

Archive maintained by Robert King, hosted by
the discipline of
statistics at the
University of Newcastle,
Australia.

Archive generated by hypermail 2.2.0, at Thu 24 Apr 2008 - 06:30:32 GMT.

*
Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help.
Please read the posting
guide before posting to the list.
*