Hello all.
 
I'm doing a simulation study where I will be making use of the 'nlme' package. I want to
loosen up the convergence criteria so that I increase the likelihood of convergence
(potentially at the cost of obtaining slightly less than ideal results). The parameters in
the function nlmeControl() control the convergence criteria. These default values can be
modified to make convergence critera more or less stringent. In an effort to get more
converging results, I first increased the iterations for 'maxIter', 'pnlsMaxIter',
'msMaxIter', and 'niterEM' by a factor of 20. I don't believe there is any harm in doing
this.
 
There are a few parameters, however, in the nlme controls that I'm not quite sure what they mean or do. For example, exactly what is 'minScale?' The help file says that it is "the minimum factor by which to shrink the default step size in an attempt to decrease the sum of squares in the 'PNLS' step." What does it mean to increase the default value? Will increasing this value lead to more convergence because the criteria is more liberal?
 
Is the tolerance criteria met when the change in the log likelihood is reduced below the
specified value, or is it a value other than the log likelihood? What about for the
pnlsTol? That is, what is the quantity whose change must be less than pnlsTol to continue?
 
Is there anything wrong with reducing 'minAbsParApVar' to zero (so that a parameter can be fixed across all individuals and thus not have a variance)?
 
Is anything, other than the help file, written about the default control parameters for
nlme? Perhaps I missed it, but I didn't see anything in Pinheiro and Bates (2000). To some extent the default parameters are arbitrary, so I'm interested in knowing if someone has opinions or has considered changing them.
 
Thanks for any thoughts,
Ken


		
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