[R-sig-ME] Predictor variable with quantifiable measurement error

[David Duffy]

On Tue, 4 May 2010, Mike Lawrence wrote:

> Hi folks,
>
> I'm wondering if there's a way to specify an lme model in a way that
> lets it take into account quantifiable measurement error in a
> predictor variable. The context is that I am interested in how fast
> people categorize faces (as male vs female) as a function of the
> attractiveness of the faces. I've measured the decision time to each
> face multiple across multiple repetitions, eg:
>
> In a separate task, I've also elicited each subject's rating of each
> face's attractiveness multiple times, eg:

> ...I'm simply not sure how to combine the data then formulate the model 
> to achieve this.

That's the kind of thing more easily handled in a multi-group SEM 
framework, I would have thought.  To do it nicely, you might carry out a 
bivariate (normal, binomial) analysis of decision time and rating type in 
MCMCglmm or BUGS.  But if you have 10 trials in the main experiment, 
surely you have some variation in the ratings there too?

Cheers, David Duffy.
-- 
| David Duffy (MBBS PhD)                                         ,-_|\
| email: davidD at qimr.edu.au  ph: INT+61+7+3362-0217 fax: -0101  /     *
| Epidemiology Unit, Queensland Institute of Medical Research   \_,-._/
| 300 Herston Rd, Brisbane, Queensland 4029, Australia  GPG 4D0B994A v