What code did you actually run to get what you labelled as 'ANOVA'? If this was anova[.lme], the default type is "sequential", whereas the t-values (from summary[.lme], I presume) are from marginal tests.
Whether sequential and marginal tests are similar or even the same is a question of balance in the design (for linear models as well). On Thu, 23 Feb 2006, Petar Milin wrote: > Hello, > I ran two lme analyses and got expected results. However, I saw > something suspicious regarding p-level for fixed effect. Models are the > same, only experimental designs differ and, of course, subjects. I am > aware that I could done nesting Subjects within Experiments, but it is > expected to have much slower RT (reaction time) in the second > experiment, since the task is more complex, so it would not make much > sense. That is why I kept analyses separated: > > (A) lme(RT ~ F2 + MI, random =~ 1 | Subject, data = exp1) > > ANOVA: > numDF denDF F-value p-value > (Intercept) 1 1379 243012.61 <.0001 > F2 1 1379 47.55 <.0001 > MI 1 1379 4.69 0.0305 > > Fixed effects: RT ~ F2 + MI > Value Std.Error DF t-value p-value > (Intercept) 6.430962 0.03843484 1379 167.32118 0.0000 > F2 -0.028028 0.00445667 1379 -6.28896 0.0000 > MI -0.004058 0.00187358 1379 -2.16612 0.0305 > > =========================================================== > > (B) lme(RT ~ F2 + MI, random =~ 1 | Subject, data = exp2) > > ANOVA: > numDF denDF F-value p-value > (Intercept) 1 659 150170.71 <.0001 > F2 1 659 17.28 <.0001 > MI 1 659 13.43 3e-04 > > Fixed effects: RT ~ F2 + MI > Value Std.Error DF t-value p-value > (Intercept) 6.608252 0.05100954 659 129.54935 0.0000 > F2 -0.008679 0.00616191 659 -1.40855 0.1594 > MI 0.009476 0.00258605 659 3.66420 0.0003 > > As you can see, in exp1 p-levels for the model and for the fixed effects > are the same, as thay should be, as far as I know. Yet, in exp2 there is > significant p for F2 in the model, but insignificant regarding F2 as > fixed factor. How is it possible? I have ran many linear models and > those two values correspond (or are the same). Anyway, how can it be to > have insignificant effect that is significant in the model? Some strange > property of that factor, like distribution? Multicolinearity? Please, > help me on that. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
