On 1/9/06, Leo Gürtler <[EMAIL PROTECTED]> wrote: > Dear alltogether, > > two lme's, the data are available at: > > http://www.anicca-vijja.de/lg/hlm3_nachw.Rdata > > explanations of the data: > > nachw = post hox knowledge tests over 6 measure time points (= equally > spaced) > zeitn = time points (n = 6) > subgr = small learning groups (n = 28) > gru = 4 different groups = treatment factor > > levels: time (=zeitn) (n=6) within subject (n=4) within smallgroups > (=gru) (n = 28), i.e. n = 4 * 28 = 112 persons and 112 * 6 = 672 data points > > library(nlme) > fitlme7 <- lme(nachw ~ I(zeitn-3.5) + I((zeitn-3.5)^2) + > I((zeitn-3.5)^3) + I((zeitn-3.5)^4)*gru, random = list(subgr = ~ 1, > subject = ~ zeitn), data = hlm3) > > fit5 <- lme(nachw ~ ordered(I(zeitn-3.5))*gru, random = list(subgr = > ~ 1, subject = ~ zeitn), data = hlm3) > > anova( update(fit5, method="ML"), update(fitlme7, method="ML") ) > > > anova( update(fit5, method="ML"), update(fitlme7, method="ML") ) > Model df AIC BIC logLik Test > update(fit5, method = "ML") 1 29 2535.821 2666.619 -1238.911 > update(fitlme7, method = "ML") 2 16 2529.719 2601.883 -1248.860 1 vs 2 > L.Ratio p-value > update(fit5, method = "ML") > update(fitlme7, method = "ML") 19.89766 0.0978 > > > > shows that both are ~ equal, although I know about the uncertainty of ML > tests with lme(). Both models show that the ^2 and the ^4 terms are > important parts of the model. > > My question is: > > - Is it legitimate to choose a model based on these outputs according to > theoretical considerations instead of statistical tests that not really > show a superiority of one model over the other one? > > - Is there another criterium I've overlooked to decide which model can be > clearly preferred? > > - The idea behind that is that in the one model (fit5) the second > contrast of the factor (gru) is statistically significant, although not > the whole factor in the anova output. > In the other model, this is not the case. > Theoretically interesting is of course the significance of the second > contrast of gru, as it shows a tendency of one treatment being slightly > superior. I want to choose this model but I am not sure whether this is > proper action. Both models shows this trend, but only one model clearly > indicates that this trend bears some empirical meaning. > > Thanks for any suggestions,
The comparisons may be more clearly shown if you create the ordered factor and a second version of the ordered factor what has the contrasts set so it produces a 4th order polynomial. That is, set hlm3$ozeit <- ordered(hlm3$zeitn) hlm3$ozeit4 <- C(hlm3$ozeit, contr.poly, 4) then define one model in terms of ozeit and a second model in terms of ozeit4. I would go further and create a new binary factor from gru that contrasted level 2 against the other three levels and use that instead of gru. For a model fit by lme I would use the one-argument form of anova to assess the significance of terms in the fixed effects. (That advice doesn't hold for models fit by lmer - at least at present.) ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html