[R] lme() direction

[Mike Lawrence]

Would it improve things if "type" were a continuous variable rather
than categorical? I chose words at the extreme ends of a valence
rating scale but I still have the raw valence ratings for each word.

On Sat, Feb 7, 2009 at 12:02 PM, Dieter Menne
<dieter.menne at menne-biomed.de> wrote:
> Mike Lawrence <mike <at> thatmike.com> writes:
>
> Thanks for the excellent reproducible sample set!
>
>> I'm most interested in the interaction between color and type, but I
>> know that there is likely an effect of word. Yet since word is not
>> completely crossed with type, simply adding it to an aov() won't work.
>> A colleague recommended I look into lme() but so far I can't figure
>> out the proper call.
>
> Without word, it would be
>
> summary(lme(rt~type*color, data=a,random=~1|id))
>
> With the interaction, the extreme would be
> summary(lme(rt~type*color*word, data=a,random=~1|id))
>
> or, less extreme
>
> summary(lme(rt~type*color+color:word, data=a,random=~1|id))
>
> but all these fail because of the rather degenerate structure
> of you data set. While lmer in package lme4 allows for a wider
> set of solutions, I currently do not see how it could help,
> but I might be wrong with p=0.5.
>
>
>               word happy joy sad grumpy
> type     color
> positive white         93  90   0      0
>         red           90  88   0      0
>         green         88  87   0      0
> negative white          0   0  88     95
>         red            0   0  91     85
>         green          0   0  88     88
>>
>
>> Another issue is whether to collapse across repetition before running
>> the stats, particularly since errors will leave unequal numbers of
>> observations per cell if it's left in.
>
> That's one of the points where you have little to bother with the lme
> approach. Collapsing would give equal weights to unequal numbers of
> repeat, and might of minor importance when not too extreme, though.
>
> Dieter
>
>
> set.seed(1)
> a=rbind(
>        cbind(
>                type='positive'
>                ,expand.grid(
>                        id=1:10
>                        ,color=c('white','red','green')
>                        ,word=c('happy','joy')
>                        ,repetition = 1:10
>                )
>        )
>        ,cbind(
>                type='negative'
>                ,expand.grid(
>                        id=1:10
>                        ,color=c('white','red','green')
>                        ,word=c('sad','grumpy')
>                        ,repetition = 1:10
>                )
>        )
> )
>
> #add some fake rt data
> a$rt=rnorm(length(a[,1]))
>
> #And because people make errors sometimes:
> a$error = rbinom(length(a[,1]),1,.1)
>
> #remove error trials because they're not psychologically interesting:
> a=a[a$error==0,]
>
> library(nlme)
> ftable(a[,c(1,3,4)])
> summary(lme(rt~type*color, data=a,random=~1|id))
>
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> and provide commented, minimal, self-contained, reproducible code.
>



-- 
Mike Lawrence
Graduate Student
Department of Psychology
Dalhousie University
www.thatmike.com

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