In addition, your model statement is odd. Note that within-S factor
Type is tested with both the type I and the type II residuals, whereas
only the latter should be used. Try this model instead:
aov.errs.ae <- aov(TrainErrs ~ idio*Type + Error(Subject/
Type),data=learnDat.ae)
or, for more clarity:
aov.errs.ae <- aov(TrainErrs ~ idio*Type + Error(Subject
+Subject:Type),data=learnDat.ae), which explicitly denotes the two
error strata.
On 17-Dec-08, at 4:00 AM, r-help-requ...@r-project.org wrote:
Your design seems to be unbalanced: multistatum aov is intended for
balanced designs. My guess is that one idio subject has two Type=1
observations: in which case try removing one of them.
On Tue, 16 Dec 2008, Harlan Harris wrote:
Hi, I'm a new R user, coming from SPSS, and without a particularly
strong
stats background.
I've got a data set that I'd like to do a mixed-design ANOVA with. No
missing values. Here's the summary:
summary(learnDat.ae)
Type Subject idio struct TrainErrs
cond
0:20 11 : 3 idio :28 ae :58 Min. : 0.00
idioae :28
2:19 12 : 3 nonidio:30 fact: 0 1st Qu.: 6.25
idiofact : 0
3:19 14 : 3 Median :11.50
nonidioae:30
15 : 3 Mean :13.40
18 : 3 3rd Qu.:16.00
2 : 3 Max. :59.00
(Other):40
Note that the TrainErrs column is the only numeric column, and I
forced
everything else to be a factor. (Is that correct?)
I then do the following:
aov.errs.ae <- aov(TrainErrs ~ (idio*Type) + Error(Subject/Type) +
(idio),
learnDat.ae)
So, idio is between-subjects and Type is within-subjects. This is
based on
examples I've found elsewhere.
summary(aov.errs.ae)
This seems to work fine:
Error: Subject
Df Sum Sq Mean Sq F value Pr(>F)
idio 1 179 179 0.89 0.36
Type 1 210 210 1.05 0.32
Residuals 17 3401 200
Error: Subject:Type
Df Sum Sq Mean Sq F value Pr(>F)
Type 2 515 258 2.44 0.103
idio:Type 2 680 340 3.22 0.053 .
Residuals 34 3595 106
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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