Samaritan <d.harliwich <at> gmail.com> writes:
> [snip]
You might want to ask follow-up questions on the R-sig-mixed-models list
> At the most basic level, I'm testing the effect of sleep deprivation on
> various behaviours (e.g. amount of time spent awake, amount of time spend
> expressing difference textures, patterns, colours etc). I take a video
> sample of each octopus every hour for 72 hours and score their behaviour in
> Jwatcher.
>
> I'm using GLMM so that I can nest Individual as a random factor within
Time,
> which I'm told will reduce the problem of making repeated measures (is
this
> effectively blocking by time and by octopus?). Currently my model looks
like
> this:
>
>
octopus.lmer<-lmer(awake~as.factor(Treatment)+Sex+Weight+(1|Time/Octopus))
You say you want to use GLMM -- presumably awake is a binary variable
that you want to treat as such? If so, you need the argument
'family=binomial' in your model. (You might want to use the
'glmer'
function instead, for clarity, although in practice R takes care of
this for you.)
>
> where "Treatment" is "0" or "1" representing
sleep-deprived or
> sleep-allowed.
As a general practice you should probably code your data as
"deprived"/"allowed" rather than 0/1: you won't have to
use as.factor
and you will automatically be able to keep track of the coding.
When I try to fit the model I get the following error> messages:
>
> Error: length(f1) == length(f2) is not TRUE
> In addition: Warning messages:
> 1: In Octopus:Time :
> numerical expression has 190 elements: only the first used
> 2: In Octopus:Time :
> numerical expression has 190 elements: only the first used
>
> This is the point at which I become lost. What does this mean? Clearly
I'm
> not doing something right, perhaps in my data preparation? So far as I can
> see the length of each of the variables is the same (although I'm no
certain
> as to what f1 and f2 refer to).
This is coming from inside lmer. My guess is that you want
to make sure Time is a factor.
dat <- expand.grid(Time=1:5,Octopus=1:5)
dat$awake <- sample(0:1,prob=c(0.5,0.5),replace=TRUE,size=nrow(dat))
dat$Time <- factor(dat$Time)
dat$Octopus <- factor(dat$Octopus)
library(lme4)
lmer(awake~(1|Time/Octopus),data=dat)
I got a similar error when both Time and Octopus were numeric.
When I turned one or the other but not both into a factor I got
different errors. When they are both factors I get an error which
is related to the fact that I don't have enough data (only one
observation per block, so the block effects are confounded with
the residual variation)
lmer(awake~(1|Time)+(1|Octopus),data=dat)
which represents a crossed effect, does work.
> If anyone could offer me some kind of advice about this I would appreciate
> it very, VERY much. Both of my supervisors have no experience with R and so
> have kind of washed their hands, so I'm alone in this and your
expertise
> would be a big help.
>
> Dean
Even though R is wonderful, if your supervisors use a different
statistical package, I would strongly recommend you stick to what
they use so that you can get properly trained, unless you are *very*
self-sufficient.