Hi:

On Wed, Sep 29, 2010 at 10:47 AM, Nicholas Lewin-Koh <nikko@hailmail.net>wrote:

> Hi Folks,
> I have an experiment with about 80 mice. Mice were randomized to
> treatment and control arms. The experiment consisted of 3 balance beams,
> Wide, Medium and Narrow. The mice were each given 3 trials on each
> balance beam (no not in random order). So the beam should get
> sequentially harder as it gets narrower. The responses were the latency,
> the time to cross the beam and the number of footslips as they crossed
> the beam. So I have beam nested in mouse and trial nested in beam. I
> tried the following model:
>

The only way beam is nested within mouse is if different sets of beams
were used for different mice. If all mice used the same set of beams, then
mice and beams are crossed factors. Recall that factor B is nested within
factor A if each level of B is associated with exactly one level of A.
Conversely, A and B are (completely) crossed if each level of A occurs
in combination with each level of B.

As far as the trials go, each mouse gets three trials, so if trial were to
be
nested within anything, it would have to be the mouse. The key question
is what is the experimental unit - the mouse or the beam?

Now that I think about it, each trial uses a different beam, so beam and
trial are confounded, no? If that's the case, the model gets much simpler...

HTH,
Dennis


> lmer(latency.s.~ Treatment + BeamSize +
> (1|ID./BeamSize/TrialNo),data=dat,na.action=na.omit)
> Error: length(f1) == length(f2) is not TRUE
> In addition: Warning messages:
> 1: In TrialNo:(BeamSize:ID.) :
>  numerical expression has 827 elements: only the first used
> 2: In TrialNo:(BeamSize:ID.) :
>  numerical expression has 827 elements: only the first use
>
> But I am not sure that this is the "right" way to specify this model.
> Ideally
> There are two questions. 1) Is the overall marginal latency lower in the
> treated animals, and 2) is the Beam size Slope
> for treated animals shallower than for untreated animals. I have looked
> at Park and Lee (2002) Statistics in Medicine 23:143-164, who give SAS
> code for a similar model. However, I am not very good at translating SAS
> to lme/lme4.
>
> Once I have this figured out, is it possible to jointly model foot slips
> and latency?
>
> Thanks for any input.
>
> Nicholas Lewin-Koh
> Senior Statistical Scientist
> Genentech
>
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