Hi everyone,

I would really like your opinion about my problems. I realized that I was
very confused in my explanation when I posted my last message, so here is a
new one:
My organisms (here nematodes) were individually (one organism per
experimental unit) exposed to 8 treatments for 3 generations (named F0, F1,
F2).  I used 7 replicates per treatment (7 replicates x 8 treatments = 56
organisms per generation). I followed the growth of individual over time.
To resume I obtained 7 growth curves for each treatment * generation
combination.

I want to model the dose-response curves using nonlinear mixed effects
regression with asymptotic model.
I have 2 fixed effects variables (treatment and generation) and one random
effects variable (nematode). To begin, I ran a script without the
co-variable treatment and generation and I follow the first steps giving by
Pinheiro and Bates to fit my nonlinear model with nls and nlsList:

don2 = groupedData(length~time|nematode, data = growth)
don3.nls = nls (length~SSasymp(time,Asym, xmid, scal), data = don2)
don3.nlslis=nlsList(length~SSasymp(time,Asym, xmid,
scal),data=don2,control=nls.control(maxiter=5200))

So far, so good. The trouble is that I also have a "lineage" effect in my
experimental plan.
I think, but I'm not sure, that we could say that "lineage" effect is
nested into treatment effect. What happens is that, for a given dose
(control for example), the nematode of the 1rst experimental unit of the
1st generation (F0) gave the nematode of the 1st experimental unit of the
2nd generation (F1), which gave the nematode of the 1st experimental unit
of the 3rd generation (F2).These 3 organisms belong to the same lineage.
There is something like a vertical effect of lineage. To give more
information, the lineages of one dose are different from lineages of the
other doses. So, in totality I have 7 lineage*8 doses, i.e. 56 lineages.

I guess I have to include this "lineage" variable with random effect but I
don't know how.

1)      I tried to include it using groupedData:

don2 = groupedData(length~time|lineage/nematode, data=growth)
don3.nls     = nls (length~SSasymp(time,Asym, xmid, scal), data = don2)
don3.nlslis = nlsList(length~SSasymp(time,Asym, xmid,
scal),data=don2,control=nls.control(maxiter=5200))
don3.nlme = nlme(don3.nlslis,control= nlmeControl(niterEM=10000,
msMaxIter=10000))

 => Error in nlme.nlsList(temp.nlslis, control = nlmeControl(niterEM =
10000,  :
  can only fit "nlsList" objects with single grouping variable

2)      I tried to include it directly in nlme using random argument:

mod1 = nlme(length~SSasymp(timeC,Asym,xmid,scal),
fixed=Asym+xmid+scal|lineage/nematode,
random=~Asym+xmid+scal|lineage/nematode, data=growth, c(Asym=1319,
xmid=166, scal=-8.4531))

 => Error in nlme.formula(taille ~ SSasymp(timeC, Asym, xmid, scal), fixed
= ~Asym +  :
  subscripts out of bounds

3)      I also tried using the lme4 library to see if I was seeing the same
problems:

mod1 = nlmer(taille~SSasymp(time, Asym, xmid,
scal)~(0+Asym|lignee/num)+(0+xmid|lignee/num)+(0+scal|lignee/num),don.exp,
c(Asym=1319, xmid=166, scal=-8.4531))

 => Error in FUN(X[[1L]], ...) :
  no loop for (break/next); jumping to top level


I would be very relieved if someone knows what to do to include the second
random effect "lineage".

Regards

______

Adeline BUISSET - GOUSSEN

Doctorante IRSN

IRSN/PRP-ENV/SERIS/LECO
Laboratoire d’Ecotoxicologie des radionucléides
Bât. 186 - BP3
13115 Saint-Paul-Lez-Durance Cedex (France)

Tél : +33 (0)4 42 19 93 40

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