From: Peter Dalgaard <p.dalgaard_at_biostat.ku.dk>

Date: Thu 23 Feb 2006 - 22:09:54 EST

Date: Thu 23 Feb 2006 - 22:09:54 EST

Petar Milin <pmilin@ff.ns.ac.yu> writes:

> 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.
*

"Type I"...

The ANOVA is progressive, so refers to the situation *after* removing MI from the model. Try anova(lmefit, Terms="F2")

-- O__ ---- Peter Dalgaard ุster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard@biostat.ku.dk) FAX: (+45) 35327907 ______________________________________________ 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.htmlReceived on Thu Feb 23 22:50:18 2006

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