Dear list,

 

I have a question about the number of iterations for the EM algorithm in
lme. 

 

To make a long story short, I am writing a batch script for the
estimation of thousands of semi-parametric mixed models. 

 

It is a longitudinal setting, in which each subject has 30 response
values. I am interested in the time-dependent effect of X on these
values (s(X)) , which I model with penalized splines. In these models,
the spline basis is modeled as random effects, which allows the
estimation of the smoothing parameter as a random component (e.g
Ruppert, Wand and Carroll Semiparametric regression, 2003). The models
also include random intercept for each individual. 

 

I cannot provide an example since the data are confidential (sorry!).

 

While most of my models converge and provide sensible results, some
models fail to converge.  I inspected the data but did not find any
obvious reasons why those models would not converge. I then increased
maxIter (the maximum number of iterations for the 'lme' optimization
algorithm) but that did not help. Then I set niterEM to 50 instead of 25
and obtained convergence for most of the models that failed to converge
first.  

 

What bothers me now is that when I increased niterEM to 200 (out of
curiosity), I 'lost' the convergence for several models! For example, a
given model converged at niterEM = 100 and niterEM = 150 but not 200.
Since I have thousands of models of this sort to estimate in batch, I
was naively hoping to set niterEM to a high value in order to obtain
convergence for all models... 

 

I double-checked the results for the models with niterEM = 50, 100, 150
and they have the same likelihood and parameter estimates, which makes
sense!

 

Does somebody have a clue about what is happening with the pattern of
convergence associated with niterEM ? Obviously, I will not be able to
set niterEM to a value that will work for all models, but I would like
to understand why, for a given model, one can lose convergence if
niterEM is increased to a large value.

 

Best regards,

MP


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