Ron: There are some misunderstandings in your message. See inline below. However, this is fundamentally not an R question -- it's about "what to do" not how to do it in R. So I suggest you post on a statistical list like stats.stackexchange.com. But as you have already noted, beware, you are likely to get a range of possibly compet8ing views. I shall add a few of my own in what follows, to which this caution also applies.
-- Bert On Wed, Jun 6, 2012 at 8:29 AM, Ron Stone <ronstone1...@gmail.com> wrote: > Dear all, > > This question may be too basic quesition for this list, but if someone has > time to answer I will be happy. I have tried to find out, but haven't found > a consice answer. > > As an example I use "Pinheiro, J. C. & Bates, D. M. 2000. Mixed-effects > models in S and S-PLUS. Springer, New York." page 225, where rats are fed > by 3 different diets over time, which body mass has been measured. > Response: Body mass, fixed effects Time*Diet, random effect ~Time|Rat. The > main question is if the interaction term is significant (i.e. growth rate). > My question is could I also look at the p-values of the main effects to say > if body mass increase significant with body mass? Come again? A typo? Do you mean: "Are the main effects of time and diet on body mass significant?" If so, by definition , yes. However, in the presence of imbalance and interactions, your interpetation of this may be wrong. > > >From Pinheiro, J. C. & Bates, D. M. (2000) > > Fixed effects: weight ~Time * Diet > Value St.error DF t-value p-value > Intercept 251.60 13.068 157 19.254 <.0001 > Time 0.36 0.088 13 4.084 0.0001 > Diet2 200.78 22.657 13 8.862 <.0001 > Diet3 252.17 22.662 157 11.127 <.0001 > TimeDiet2 0.60 0.155 157 3.871 0.0002 > TimeDiet3 0.30 0.156 157 1.893 0.0602 > > As stated by Pinheiro, J. C. & Bates, D. M. (2000), the growth rate of diet > 2 (TimeDiet2) differs significantly from diet 1. Allthoug could I from this > also say that body mass increase significantly with time for diet 1? This is the R part of your question. You do not understand what contrasts are in linear models and, in particular, R's default choice of contrasts (which actually really aren't contrasts). See ?contrasts and especially ?contr.treatment. The point here is that with 3 diets there are only 2 diet "effects" and their corresponding interactions with time that exist. These can be chosen in any of several different (in theory infinitely many) ways. By default, R chooses Diet 1, time 0 as the "control" (given by the intercept term; note that there can be no "no diet" against which to compare Diet 1) and the Diet 2 and Diet 3 main effects and their corresponding interactions with time represent the differences of these Diets against the Diet 1 control and their differences in growth rate vs Diet 1. So bottom line: Your question is nonsense. Like > this: f(x) = 251.60 (+/-13.068) + 0.36 x (+/- 0.088), t = 4.084, p = > 0.0001? I have seen different places that it people claiming that when the > interaction is significant then it is wrong to interpret p-values for the > main effects. Is it more proper to split the data and run the test (weight > ~Time) for each diet seperately, when looking at the simple effect of time > on body mass? I would agree, with the caveat that P values should be ignored. Bottom Line: (keep my caution in mind): 1. Plot your data informatively. lattice or ggplot style trellis plots would be useful here. See also http://addictedtor.free.fr/graphiques/ where you may get some ideas. This is likely to be more useful than formal statistics. 2. Consult a local statistician. Spending an hour or two of time with a local statistician (i.e. anyone with statistical expertise who understands linear models, not necessarily someone with a statistical degree) would be better than any advice that you could obtain here, including, paradoxically, mine. -- Bert > > Best regards Ron > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. -- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm ______________________________________________ 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.
