From: Patrick Giraudoux <patrick.giraudoux_at_univ-fcomte.fr>

Date: Sat 12 Nov 2005 - 06:12:17 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 Sat Nov 12 06:24:43 2005

Date: Sat 12 Nov 2005 - 06:12:17 EST

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

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

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 Sat Nov 12 06:24:43 2005

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