[R] model comparison and Wald-tests (e.g. in lmer)

[Thomas Petzoldt]

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.