And if I decided to ignore the "type" variable altogether and simply
use the continuous "valence" variable, this is what I'd use?
summary(lme(
fixed = rt~valence*color
, data = a
,random = ~1|id
))
I also have continuous luminance measurements of each color that vary
from participant to participant (we used different monitors). If I
were interested in luminance *instead* of color, would the following
be appropriate, or do I need to do something special to account for
the fact that each participant has different values of luminance?
summary(lme(
fixed = rt~valence*luminance
, data = a
,random = ~1|id
))
On Sat, Feb 7, 2009 at 12:34 PM, Dieter Menne
<dieter.me...@menne-biomed.de> wrote:
> Mike Lawrence <mike <at> thatmike.com> writes:
>
>>
>>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.
>>
>> >
>> > 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))
> ..
> Something like
>
> summary(lme(rt~type*color+color:as.numeric(word), data=a,random=~1|id))
>
> (please replace as.numeric() by the raw valence, the example above it
> simply wrong)
>
> could gain you a few degrees of freedom if you are willing to accept the
> linear hypothesis. And as there is something like raw valence, one should
> not throw away details about a-priori ordering in favor of a categorical
> hypothesis.
>
> Dieter
>
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>
--
Mike Lawrence
Graduate Student
Department of Psychology
Dalhousie University
www.thatmike.com
Looking to arrange a meeting? Check my public calendar:
http://www.thatmike.com/mikes-public-calendar
~ Certainty is folly... I think. ~
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