Dear Roger, Thanks for your comment. Yes, I understand that the p values do not refer to the probability of Type I error. I think I will use the CI as they are for the moment. Regarding the TukeyHSD procedure, I can only tell you that it is more powerful than Bonferroni corrections when you plan to make many or all possible mean comparisons. However I don't think it can be applied to the means obtained with the MCMC simulation. I would also like to learn more about this topic.
Thanks, Francisco On 8/6/07, Roger Levy <[EMAIL PROTECTED]> wrote: > Francisco Torreira wrote: > > Dear Roger and Florian, > > > > Thanks so much for your comments. A model with random slopes but no > > random intercepts (e.g. (0+type|spk)) also seems to lead to > > singularity. As I said in my previous message, this happens too for a > > model with both random intercept and slop (e.g. (1+type|spk)). I > > understand Roger's suggestion to merge levels 'e' and 'i'. However, if > > I am fitting the model, it's precisely to compare the level means :-) > > > > I have therefore fitted a model with a random intercept and calculated > > the CI for the level means using Baayen's pvals.fnc(). > > I suppose that the CI obtained this way are not equivalent to the ones > > obtained with post-hoc comparison procedures (e.g. TukeyHSD). Does > > anyone have an idea how to do this with a mixed model? > > Dear Francisco, > > I don't really know much about the Tukey HSD procedure (can you suggest > a reference?), but the Bonferroni correction, for example, could be > applied to the t-test-based p-values returned by pvals.fnc(). The > MCMC-based confidence intervals are Bayesian confidence intervals and > thus represent the model's posterior beliefs about the likely parameter > values, not degree of unlikeliness of seeing the data under the null > hypothesis. As such, they don't seem very philosophically compatible > with something like the Bonferroni correction, which at heart asks the > question "if my null hypothesis is really true, how many times would I > expect to get at least one false positive if I conduct multiple tests?". > On the other hand, in the simulated-data cases of the Baayen et al. > paper, the MCMC-based p-values are generally pretty close to the > p-values you'd want for a classical hypothesis test, so there's nothing > to stop you in practice from applying the ordinary Bonferroni correction > to the MCMC-derived p-values. > > Hope that is useful. I'd be curious to hear what other people have to > say about this. > > Roger > -- Francisco Torreira PhD Candidate in Hispanic Linguistics University of Illinois at Urbana-Champaign https://netfiles.uiuc.edu/ftorrei2/www/index.html tel: (+1) 217 - 778 8510 _______________________________________________ R-lang mailing list [email protected] https://ling.ucsd.edu/mailman/listinfo.cgi/r-lang
