Displaying 3 results from an estimated 3 matches for "fact3l".
Did you mean:
fact3
2004 Aug 12
0
Re: R-help Digest, Vol 18, Issue 12
...> anova(lme4_1)
> Analysis of Variance Table
>
> Df Sum Sq Mean Sq Denom F value Pr(>F)
> fact1I 1 2.709e-07 2.709e-07 48 21.9205 2.360e-05
> ***
> fact2I 1 9.229e-08 9.229e-08 48 7.4665 0.008772 **
> fact3L 1 4.906e-08 4.906e-08 48 3.9691 0.052047 .
> fact3M 1 4.326e-07 4.326e-07 48 34.9972 3.370e-07 ***
> fact1I:fact2I 1 1.095e-07 1.095e-07 48 8.8619 0.004552 **
> fact1I:fact3L 1 8.988e-10 8.988e-10 48 0.0727 0.788577
> fa...
2004 Aug 11
1
Fwd: Enduring LME confusion… or Psychologists and Mixed-Effects
In my undertstanding of the problem, the model
lme1 <- lme(resp~fact1*fact2, random=~1|subj)
should be ok, providing that variances are homogenous both between &
within subjects. The function will sort out which factors &
interactions are to be compared within subjects, & which between
subjects. The problem with df's arises (for lme() in nlme, but not in
lme4), when
2004 Aug 10
4
Enduring LME confusion… or Psychologists and Mixed-Effects
Dear ExpeRts,
Suppose I have a typical psychological experiment that is a
within-subjects design with multiple crossed variables and a continuous
response variable. Subjects are considered a random effect. So I could model
> aov1 <- aov(resp~fact1*fact2+Error(subj/(fact1*fact2))
However, this only holds for orthogonal designs with equal numbers of
observation and no missing values.