# Interpretation of lme results with intercorrelation between fixed factors

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## Interpretation of lme results with intercorrelation between fixed factors

 I�m running a mixed model analysis with 2 fixed factors that are intercorrelated using the lme function and I�m having difficulties in interpreting the results. As I�m quite novice I�ll try to use a very simple example. My model is lme(Y~A*B). A has 3 levels (1, 2 and 3) and B has 2 levels (I and II). My results are something like this: P statistic B:II P=0.01 A:2 P=0.01 A:3 P=0.09 II*2 P=0.61 II*3 P=0.031 My question is as follows. I understand that R keeps a level of each factor and reports any statistical differences to it. So in this example it reports that II is different than I and 2 against 3. However when it comes to the intercorrelation, what does it report? Does it compare II*2 and II*3 to II*1 and if so what happens with I*2 and I*3? Or does it compare II*2 to I*2 and II*3 to I*3 and if so what happens to I*1 and II*1? Thank you Vasillis         [[alternative HTML version deleted]] ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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## Re: Interpretation of lme results with intercorrelation between fixed factors

 > On Mar 10, 2017, at 3:22 AM, Vasillis Papathanasiou <[hidden email]> wrote: > > I�m running a mixed model analysis with 2 fixed factors that are intercorrelated using the lme function and I�m having difficulties in interpreting the results. > As I�m quite novice I�ll try to use a very simple example. I'm guessing this is perhaps the `lme` function in pkg:nlme. It's not really an appropriate question for rhelp in any event. See the Posting Guide (link below). > > My model is lme(Y~A*B). A has 3 levels (1, 2 and 3) and B has 2 levels (I and II). > My results are something like this: > Better to include actual output. I think most statisticians would advise focussing on model comparisons rather than p-values of individual coefficients. The experts hang out at: https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models... and they tend to be more welcoming for "interpretation" questions. Or post to CrossValidated.com where this would be on topic. That group generally tries to avoid purely R questions, but your question is so devoid of actual R (no code and no actual output) that it would probably not be closed for that reason. Best; David. > > > P statistic > > B:II > > P=0.01 > > A:2 > > P=0.01 > > A:3 > > P=0.09 > > II*2 > > P=0.61 > > II*3 > > P=0.031 > > > My question is as follows. I understand that R keeps a level of each factor and reports any statistical differences to it. So in this example it reports that II is different than I and 2 against 3. However when it comes to the intercorrelation, what does it report? Does it compare II*2 and II*3 to II*1 and if so what happens with I*2 and I*3? Or does it compare II*2 to I*2 and II*3 to I*3 and if so what happens to I*1 and II*1? > > Thank you > > > Vasillis > > > [[alternative HTML version deleted]] > > ______________________________________________ > [hidden email] mailing list -- To UNSUBSCRIBE and more, see > 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. David Winsemius Alameda, CA, USA ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.