Dear expeRts, there is obviously a general trend to use model comparisons, LRT and AIC instead of Wald-test-based significance, at least in the R community. I personally like this approach. And, when using LME's, it seems to be the preferred way (concluded from postings of Brian Ripley and Douglas Bates' article in R-News 5(2005)1), esp. because of problems with the d.f. approximation.
But, on the other hand I found that not all colleagues are happy with the resulting AIC/LRT tables and the comparison of multiple models. As a compromise, and after a suggestion in Crawley's "Statistical computing" one may consider to supply "traditional" ANOVA tables as an additional explanation for the reader (e.g. field biologists). An example: one has fitted 5 models m1..m5 and after: >anova(m1,m2,m3,m4,m5) # giving AIC and LRT-tests he selects m3 as the most parsimonious model and calls anova with the best model (Wald-test): >anova(m3) # the additional explanatory table My questions: * Do people outside the S-PLUS/R world still understand us? * Is it wise to add such an explanatory table (in particular when the results are the same) to make the results more transparent to the reader? * Are such additional ANOVA tables *really helpful* or are they (in combination with a model comparison) just another source of confusion? Thank you! Thomas P. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html