st: RE: effect size in nonlinear regression

[Nick Cox <n.j.cox@durham.ac.uk>]

I don't know what you mean precisely by effect size. I can guess, but I do not know. 

But I see no problem in using R^2, meaning square of correlation between observed and predicted, as a descriptive measure for nonlinear regression. That does not rule out other interpretations for R^2 defined in other ways. This viewpoint is developed at 

FAQ     . . . . . . . . . . . . . . . . . . . . . . . Do-it-yourself R-squared
        . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  N. J. Cox
        9/03    How can I get an R-squared value when a Stata command
                does not supply one?
                http://www.stata.com/support/faqs/stat/rsquared.html

It is arguable that much confusion has arisen because in simple linear models various different viewpoints all produce the same algebra. It may be you are focusing on nonlinearity when the bigger problems of interpretation often arise from non-additivity. 

Also, even people who should know better can be too attracted by the idea of summarizing model merit by single figures of merit. Me too, some of the time. 

Nick 
n.j.cox@durham.ac.uk 

Airey, David C

In linear regression or ANOVA, effect size can be eta^2 or omega^2, the amount of explained variation in the sample or population, respectively. Do these concepts translate to nonlinear regression? Does anyone have any favorite references discussing statistical power and effect size measures for nonlinear regression? I'm guessing these concepts don't translate to nonlinear regression, because I see some cautions about interpreting R^2 in nonlinear regression. I'm trying to understand how one expresses a change in a nonlinear regression parameter in terms of some kind of standardized effect size.

*
*   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/