Hi Peter, thanks a lot for your help. Very much appreciated.
Cheers, Julia Peter Dalgaard wrote: > > Julia S. wrote: >> Hi there, >> >> thanks for your help. I did read Bates statement several times, and I am >> very glad and thankful that many statisticians spend much time on this. >> The >> problem is, as Dieter pointed it out, that many "end users" often have to >> use statistics without being able to fully understand the math behind it. >> Because if they would spend as much time on that as statisticians do, >> they >> wouldn't be able to do what they do where they use statistics for. >> And, no, I don't expect that a "simple" answer exists, but it might be >> that >> somebody had a similar problem like me before and may have a convincing >> line >> for a referee at hands. I have problems reformulating what I read here in >> my >> own words. >> >> Dieter: when you write: >> "but to use lme instead when possible" do you mean that when using lme >> the >> F-stats are correct? Because I assumed that the problem would be the same >> with lme. >> >> Julia >> > They aren't... And they can be badly wrong in some cases. > > At this stage, I think the best one can do is to get a feeling for > whether the DF would be "large" and if so, convince the referee to > accept an asymptotic chi-square test (Wald or LRT type). > > I think that the rationale for requiring authors to state the DF is not > so much that journals believe in mighty SAS, but that they want to be > able to catch completely wrong analyses, like when people compare two > groups of each 5 rats and get a denominator DF of around 100 because > there were 10 (correlated) measurements on each rat and no between-rats > variation in the model. > > As for figuring out whether or not you have large DF; if you have a > nearly balanced design. it might be worth looking into what aov() says > would be the DF for the same model with balanced data. > > (And in any case, all DF-type corrections are in a sense wrong because > they depend on 3rd and 4th moments of the Gaussian distribution, and > your data probably aren't perfectly Gaussian.) > > -- > O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B > c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K > (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 > ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > -- View this message in context: http://www.nabble.com/lme-and-lmer-df%27s-and-F-statistics-again-tp19835361p19894366.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
