Henrik, thank you!  This is great.

Can I ask, do the sum to zero contrasts/type 3 anova tests take into
account the random effects in these estimations?

Thank you once again!

Sarah


On Wed, Sep 18, 2013 at 6:21 PM, Henrik Singmann <
henrik.singmann@psychologie.uni-freiburg.de> wrote:

> Hi Sarah,
>
> One way to globally set sum to zero contrasts is by setting the contrasts
> via options:
> options(contrasts=c('contr.**sum', 'contr.poly'))
>
> This is automatically done when loading afex which also provides you with
> the overall effect you are interested in (using the Kenward-Rogers
> approximation):
>
> require(afex)
>
> dat <- read.table("http://pastebin.**com/raw.php?i=bHug5kTt<http://pastebin.com/raw.php?i=bHug5kTt>",
> header = TRUE)
>
> mixed(DV~TMT1*TMT2+(1|Block/**TMT1), dat)
>
> ##        Effect     stat ndf ddf F.scaling p.value
> ## 1 (Intercept) 377.3464   1   5         1  0.0000
> ## 2        TMT1   3.2653   1   5         1  0.1306
> ## 3        TMT2  13.2433   1  10         1  0.0045
> ## 4   TMT1:TMT2  27.0271   1  10         1  0.0004
>
>
> Alternatively you can get the same results from car::Anova:
>
> Anova(m1)
> ## Analysis of Deviance Table (Type II Wald chisquare tests)
> ##
> ## Response: DV
> ##             Chisq Df Pr(>Chisq)
> ## TMT1       3.2653  1  0.0707600 .
> ## TMT2      13.2433  1  0.0002736 ***
> ## TMT1:TMT2 27.0271  1  2.006e-07 ***
> ## ---
> ## Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> As your design is fully balanced this is equivalent to type three tests
> (the default for afex):
> Anova(m1, type = 3)
>
> Hope that helps.
>
> Cheers,
> Henrik
>
>
> Am 18.09.2013 18:20, schrieb Sarah Dryhurst:
>
>> Hi Ben,
>>
>> I'm sorry to dredge up this post again - I thought my reply had sent but I
>> checked and it was sat in my drafts folder.
>>
>> My question was on the sum to zero contrasts you suggested.  Is there a
>> recommended way to calculate these in R, or any references I can read
>> about
>> them (I am a complete novice).  I was looking into using anova or drop1 on
>> my models to get these "average" effects of the main effects and
>> interaction, however these two only seem to take into account fixed
>> effects
>> in their calculations (do they use Wald statistics?) which seems pointless
>> to me.  What is your view on these two?  My design is a balanced one...  I
>> know the glmm wiki recommends using a mcmc approach:
>>
>> "Tests of effects (i.e. testing that several parameters are simultaneously
>> zero)
>>
>>  From worst to best:
>>>
>>
>>     - Wald chi-square tests (e.g. car::Anova)
>>     - Likelihood ratio test (via anova or drop1)
>>     - *For balanced, nested LMMs* where df can be computed: conditional
>>     F-tests
>>     - *For LMMs*: conditional F-tests with df correction (e.g.
>> Kenward-Roger
>>     in pbkrtest package)
>>     - MCMC or parametric, or nonparametric, bootstrap comparisons
>>
>>     (nonparametric bootstrapping must be implemented carefully to account
>> for
>>     grouping factors)"
>>
>>
>> I have done this via pvals.fnc(m1) in languageR but it still only gives
>> the
>> a similar output to my original (with no way of knowing the average
>> effects).  My concern is that the average effects seem to be what
>> supervisors/reviewers want reported i.e. the effect of Treatment1, rather
>> than the effect of Treatment1 level A compared to Treatment 1 level B
>> etc....
>>
>> Any thoughts would be much appreciated!  I'm finding it hard to find a
>> consensus anywhere.  It is difficult to track down examples of reporting
>> these things - most focus seems to be on interpretation.  Thank you once
>> again for your advice.
>>
>> Sarah
>>
>>
>> On Mon, Sep 9, 2013 at 10:54 PM, Ben Bolker <bbolker@gmail.com> wrote:
>>
>>  Sarah Dryhurst <s.dryhurst@...> writes:
>>>
>>>
>>>> Hi Ben,
>>>>
>>>> Thank you for your reply!  I don't really want to calculate the main
>>>> effects as it doesn't make much biological sense (to me!).  I just
>>>> wasn't sure whether this was "required" in terms of statistical
>>>> reporting.  That interaction effect is what I am interested in
>>>> largely, as it's the combined effect of the different treatments that
>>>> is my focus.
>>>>
>>>
>>>    I think it would still be worth reporting the main effects, as
>>> their size puts the size of the interaction in perspective (e.g. I
>>> would generally like to be able to judge the size of the interaction
>>> *relative to* the main effects, not just its magnitude/t statistic/
>>> p value ...
>>>
>>>
>>>> With regards to the lack of variance at the Block level, would you
>>>> recommend dropping this level here?  It doesn't seem to make too much
>>>> sense to keep it there...
>>>>
>>>
>>>    Doesn't matter too much, since the results will be almost identical.
>>> May be worth checking without, to double-check that the zero-variance
>>> result hasn't thrown off the optimization, but I would personally
>>> probably err on the side of reporting it (or say that it was in
>>> the original model but estimated as being effectively zero).
>>>
>>> ______________________________**_________________
>>> R-sig-mixed-models@r-project.**org <R-sig-mixed-models@r-project.org>mailing list
>>> https://stat.ethz.ch/mailman/**listinfo/r-sig-mixed-models<https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models>
>>>
>>>
>>
>>
>>
> --
> Dipl. Psych. Henrik Singmann
> PhD Student
> Albert-Ludwigs-Universität Freiburg, Germany
> http://www.psychologie.uni-**freiburg.de/Members/singmann<http://www.psychologie.uni-freiburg.de/Members/singmann>
>
>
> ______________________________**_________________
> R-sig-mixed-models@r-project.**org <R-sig-mixed-models@r-project.org>mailing list
> https://stat.ethz.ch/mailman/**listinfo/r-sig-mixed-models<https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models>
>



-- 
NERC PhD Student
Community Ecology and Global Change
Department of Biology
Imperial College, London
email: sarah.dryhurst08@imperial.ac.uk

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