[R] lme model specification

[Bill Simpson]

I have been asked to analyse the results of (what is to me) a very
complicated experiment.

The dependent measure is the estimated distance, which is measured as a
function of the actual distance. There are also several other IVs.

The plot of log estimated distance as a function of log distance is
linear. So in the rest of the analysis I will use logestimate and
logdistance.

My plan is to see how the other IVs affect the slope and intercept of
this linear relationship between logestimate and log distance.

What complicates everything is that each datum point is not independent.
Rather, many data points come from each subject.

So:
* Each subject gets many objects at many distances which he has to
estimate.
* Each subject repeats this experiment using 4 colours of LEDs.
* Each subject repeats this experiment on 4 different sessions.
* Half the subjects do this under starlight, half under moonlight.
* Half the subjects do it with feedback and half without.

So some of these variables are within subjects and some between. I think
lme is a good way to proceed. But I am hung up on how to specify the
model

fit<-lme(fixed=logestimate~logdistance*session*illum*feedback,
random=???|subject???, data=df1)

I am familiar with the steps of model building using lm(), exploring
different models etc, so I think I will be OK once I get the idea of
specifying the basic lme model.

I have Pinheiro and Bates (2000) here.

Thanks very much for any help

Bill Simpson