R help - Nov 2005 - repeated values, nlme, correlation structures

Dear listers,

As an exercise, I am trying to fit a logistic model with nlme.  Blue tit 
pulli (youngs) were weighted occasionnally (for field reasons) along 
time in 17 nestboxes. Individuals where not idenfied but their age was 
known. This means that for a given age several measurements were done 
but individuals could not be identified from a time to the other. This 
makes repeated values for a given age group in each nestbox. The aim is 
to get an acceptable growth curve (weight against age).

As far as repeated values cannot be handled with standard coStruct 
classes of nlme, I have done a first fit with nlme using the mean of 
each group. Comparing several models, the best fit is:

modm0c<-nlme(pds~Asym/(1+exp((xmid-age)/scal)),
    fixed=list(Asym~1,xmid~1,scal~1),
    random=Asym+xmid~1|nichoir,data=croispulm,
    start=list(fixed=c(10,5,2.2)),
    method="ML",
    corr=corCAR1()
    )

with pds = weight, age = mean age of each age group, nichoir = nestbox 
(a factor of 17 levels)

Based on the empirical autocorrelation function of the normalised 
residuals drawn from this model one can acceptaly assume that  the 
normalized residuals behave like uncorrelated noise.

Though this could be quite satisfying at first sight,  I am quite 
frustrated with starting from the mean weight of each age group, thus 
not being capable to manage and incorporate the variability around the 
mean weight of each age group in the model. My bible is Pinheiro & Bates 
(2000), but I did not find an example to board this problem.

Is there an affordable way (=not that much complicated for a biologist 
familiar to some general statistics) to handle this in nlme?

Any hint?

Patrick

Dear listers,

My request of last week seems not to have drawn someone's attention. 
Suppose it was not clear enough.

I am coping with an observational study where people's aim was to fit 
growth curve for a population of young blue tits. For logistic reasons, 
people have not been capable to number each individual, but they have a 
method to assess their age. Thus, nestboxes were visited occasionnally, 
youngs aged and weighted.

This makes a multilevel data set, with two classification factors:

- the nestbox (youngs shared the same parents and general feeding 
conditions)
- age in each nestbox (animals from the same nestbox have been weighed 
along time, which likely leads to time correlation)

Life would have been heaven if individuals were numbered, and thus nlme 
correlation structure implemented in the package be used easy. As 
mentioned above, this could not be the case. In a first approach, I 
actually used the mean weight of the youngs weighed at each age in nest 
boxes for the variable "age", and could get a nice fit with
"nestbox" as
random variable and corCAR1(form=~age|nestbox) as covariation structure.

modm0c<-nlme(pds~Asym/(1+exp((xmid-age)/scal)),
    fixed=list(Asym~1,xmid~1,scal~1),
    random=Asym+xmid~1|nestbox,data=croispulm,
    start=list(fixed=c(10,5,2.2)),
    method="ML",
    corr=corCAR1(form=~age|nestbox)
    )

Assuming that I did not commited some error in setting model parameters 
(?), this way of doing is not fully satisfying, since using the mean of 
each age category as variable  leads to a  loss of information regarding 
the variance on the weight at each age and nestbox.

My question is: is there a way to handle repeated values per group (here 
several youngs in an age category in each nestbox) in such a case?

I would really appreciate an answer, even negative...

Kind regards,

Patrick