Dear users of R mixed effect models,

I would like to ask your opinion about the problems I encounter.

I am a PhD student working in a ecotoxicology laboratory. My model organism
is the nematode.
I followed the growth (variable length) over time according to 8 doses and
3 generations (fixed effects).
For a given dose 7 replicates were tested by generation, knowing that
"organism 1" of the generation 3 belongs to the same lineage of the
"organism 1" of the generations 2 and 1. It is the same for 6 others.
"lineage" is so a variable with a random effect.

My datasheet was built like this:

nematode|lineage|time|replicate|dose|length|generation

To analyse the growth, I use an asymptotic model with the
"nls->nlsList->nlme" approach.

Here are my problems and questions :
(i)  I guess I have to use the random argument of nlme but how to use it to
fit a a first non linear mixed effect model without fixed effect using the
nls->nlsList->nlme approach? with random=1|lineage?

don2 = groupedData(length~time|nematode, data = don.exp, outer =
~dose*generation)

don3.nls = nls (length~SSasymp(time,Asym, xmid, scal), data = don2)

don3.nlslis=nlsList(length~SSasymp(time,Asym, xmid, scal
),data=don2,control=nls.contro l(maxiter=5200))

don3.nlme = nlme(don3.nlslis,*random=1|lineage*, control=
nlmeControl(niterEM=10000, msMaxIter=10000))

(ii) How to introduce the random effect "lineage" in my script? I used
groupedData but it seems do not work...

(iii) Even if my script does not include yet the random effect "lineage", I
have a convergence problem for the last command (don3.nlme). Even
increasing the number of iterations to 100,000 convergence is not reached.
Is anyone know what I should do?

Thank you in advance for the help you can provide. Any suggestions and / or
comments are also welcome.

Yours faithfully,

Adeline

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