Dear list users,

trying to run a multisite longitudinal model with multiple response which
are multiple similar measures for each subject and thus could covary.
I did it by reshaping the data setting the variable as factor in the model
as follows:
>
lmer1<-lmer(value~0+variable/(condition/time+confounds...)+other_confounds...+(1|site_id)+(-1+variable|subject_id),data=data1,REML=FALSE)
but running such model outputs the following warning:
      caught warning: failure to converge in 10000 evaluations
and trying to add a control parameter to change number of evaluation as:
> ...,control=list(maxFN=20000))
which add another warning to the output:
      caught warning: unused control arguments ignored
But in both cases a results is stored in the lmer1 variable.
My questions are:
-Is that the right way to model such a multivariate model? How many
response is it reasonable to include in a faked multivariate model? To what
I can understand, adding more response would virtually increase the number
of observations thus not decreasing DFs number as part of the random/fixed
factors are "variable independent". I am wrong?

-Does the first warning implies that the outputted estimate is wrong?

-Why are the control arguments not taken into account? (sessionInfo outputs
lme4_0.99999911-0 as version) Does it makes any sense trying to change
these to remove the warning?

Thanks for your help.
Bests.

basile

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