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


Now the problem:

> model.tables(aov.errs.ae,"means")
Error in outer(rownames(efficiency), colnames(efficiency), paste)[eff.used]
:
  invalid subscript type 'list'
In addition: Warning message:
In any(efficiency) : coercing argument of type 'double' to logical

All the examples and manuals I've found said this should work. When I did a
fully between-subjects ANOVA on another data set, I had no problem with
model.tables. I have no idea what to make of this error message. I've tried
a number of variations on things, with no improvement.

This is R version 2.7.2 (2008-08-25), running on Redhat, x86_64.

Suggestions? Thanks!

 -Harlan

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