[R] absurd computiation times of lme

[Christof Meigen]

Hi Renaud,

Renaud Lancelot <lancelot at sentoo.sn> writes:
> It is because of the random effects (the estimations of the var-cov
> random-effect matrix is very computer intensive). I think you would need
> a very large data set to be able to estimate so many random-effect
> parameters (21 parameters: 6 variances and 15 covariances). 

Well, in the case of the children I do have quite large datasets,
around 1000 children with altogether much more than 5000 measurements.

> May be it
> would be easier to fit non-linear models. I am not a specialist of human
> growth, but what about models like JPPS ? 

JPPS, which models a whole growth curve, needs the adult height of the
child. Still it is very hard to fit, and optimization sometimes fails
to converge even on what looks like perfect data, see for example
"Mathematical models of growth in stature throuout childhood", by
Ledford & Cole.

JPPS has 6 Parameters, which is one more than I would use for the 
little kids. The point in using a spline basis was to make quite
little a priori assumptions on the model but let "lme" do the
job of deciding what the curve should look like. Unfortunatly, this
seems more than it can handle. 

I just thought it would be a good way to avoid that kind of
"why did you chose than model" questions, which might lead to
very annoing discussions if someone puts effort into rejecting
all of your views.

        Christof


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