Re: [R] lme and lmer df's and F-statistics again
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.
Re: [R] lme and lmer df's and F-statistics again
Hm. Bert Gunter wrote: that even the most technical aspects of the discipline can be made manifest to anyone with half a brain and a stat 101 course under their belt. I don't think this is something I can use in a rebuttal. The reviewer may be offended and reviewers are people one does not want to offend. In general, I disagree. This get a bit philosophical, but well. I think there are some occasions where it is important to explain complicated things in few, easy to understand sentences to laymen (even if that means loss of preciseness). That has to be done (and was done in the past) with the other examples you give (thermodynamics, Krebs cycle ect.) fairly often, especially when politics are involved (think LHC, stem cells, or, even the structure of the DNA). Even for very difficult topics this needs to be done. I think our (maybe most challenging) duty as researchers paid by tax money is also to explain our sometimes very complicated research to laymen in an easy understandable manner. Albeit it is of course not your duty to explain it to me on this list, if you are offended by my attitude. Isn't it the most normal thing to ask for an explanation when somebody doesn't understand something? I've learned that asking is a good way of learning new things. Sorry if that offended you. Confused, Julia Cheers, Julia -- View this message in context: http://www.nabble.com/lme-and-lmer-df%27s-and-F-statistics-again-tp19835361p19877014.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.
Re: [R] lme and lmer df's and F-statistics again
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 -- View this message in context: http://www.nabble.com/lme-and-lmer-df%27s-and-F-statistics-again-tp19835361p19876728.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.
[R] lme and lmer df's and F-statistics again
Dear R-users, I did do a thorough search and read many articles and forum threads on the lme and lmer methods and their pitfalls and problems. I, being not a good statistician but a mere user, came to the conclusion that the most correct form of reporting statistics for a mixed linear model would be to report the parameter estimates and SEs, and, if the sample size is considerably high, p-values of a student's t-test on those. Now, I did that in my article and I got a response from a reviewer that I additionally should give the degrees of freedom, and the F-statistics. From what I read here, that would be incorrect to do, and I sort of intuitively also understand why (at least I think I do). Well, writing on my rebuttal, I find myself being unable to explain in a few, easy to understand (and, at the same time, correct) sentences stating that it is not a good idea to report (most likely wrong) dfs and F statistics. Can somebody here help me out with a correct explanation for a laymen? Any help is dearly appreciated, Jule -- View this message in context: http://www.nabble.com/lme-and-lmer-df%27s-and-F-statistics-again-tp19835361p19835361.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.
