On Fri, Sep 07, 2007 at 08:58:32PM +0800, Lngmyers wrote: > > All this talk about p-values for LME and mcmc reminds me of an old > question. To compare the sizes of the coefficients within a single > ordinary linear regression model, we can standardize them (by > multiplying each by sd(x)/sd(y)) and look at the difference in their > sizes. But we're not allowed to test whether this difference is > statistically significant. I don't know enough math to know why not. > > Why couldn't we test the null hypothesis by resampling? Compute the > standardized regression coefficients for each new sample, and count how > many samples show a difference at least as large as the difference for > the actual data. > > Is there any literature on this? Any a priori objections?
If I've understood your question correctly, you are asking about a linear regression model with response, say z, and two predictors x and y: K: z = a + mx + ny + error and you wish to know whether H0: m=n. If so, anova(lm(z~x+y),lm(z~I(x+y))) should be valid under the usual conditions. -- James S. Adelman, Department of Psychology, University of Warwick, COVENTRY, () ascii ribbon campaign - against html e-mail CV4 7AL. /\ www.asciiribbon.org - against proprietary attachments _______________________________________________ R-lang mailing list R-lang@ling.ucsd.edu https://ling.ucsd.edu/mailman/listinfo.cgi/r-lang
