Let's do the simplest things first: I'd start with normal
probability plots of log(t_ij). If that all looked sensible, I'd then
use lme to model log(t_ij) with subject as a random effect and
(probably) que as a fixed effect.
Then I'd probably try something like glmmPQL in library(MASS) to
leverage whatever I got into logistic regression models with, again,
subject as a random effect and que and log(t_ij) as possible explanatory
variables.
Regarding "lme", I highly recommend Pinheiro and Bates (2000)
Mixed-Effect Models in S and S-Plus (Springer). For glmmPQL, see
Venables and Ripley (2002) Modern Applied Statistics with S, 4th ed.
(Springer). I'd also search the archives for "generalized linear mixed
models", as there are other R packages that offer related capabilities.
spencer graves
Ravi Varadhan wrote:
> Hi,
>
>
>
> This is not an R related question and I apologize for that, but given the
> brain power of the R community it is hard for me to resist posting this
> here.
>
>
>
> I have a problem where each participant is shown a series of visual cues
> (displayed on a computer screen in a random order) and asked to respond by
> pressing a button (from a finite number of buttons) that corresponds to the
> correct answer. The data is of the form (r_ij , t_ij), where r_ij = 1
> indicates correct response by the i-th subject on the j-th cue, and t_ij is
> the time taken to respond. Are there statistical models to describe this
> set up? Any references or suggestions would be greatly appreciated.
>
>
>
> Thanks very much,
>
> Ravi.
>
>
> [[alternative HTML version deleted]]
>
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