From: Bill Simpson <william.simpson_at_drdc-rddc.gc.ca>

Date: Wed 18 Jan 2006 - 06:10:03 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Jan 18 06:17:19 2006

Date: Wed 18 Jan 2006 - 06:10:03 EST

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

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Jan 18 06:17:19 2006

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