[R] understanding the verbose output in nlme

Spencer Graves spencer.graves at pdf.com
Sun Jun 4 20:49:48 CEST 2006

	  I don't know, but if it were my question, I think I could find
out by making local copies of the functions involved and stepping
through the algorithm line by line using "debug" (see, e.g.,

	  Have you read Pinheiro and Bates (2000) Mixed-Effects Models
in S and S-Plus?  If no, I encourage you to do so.  Over the past 4
years or so, I've probably spent more time with this book and referred 
more people to it than any other.  Doug Bates is a leading original 
contributor in this area, and I believe you will find this book well 
worth your money and  your time.

	  Regarding "the numbers under subjectno1-6", I'm guessing that
these may be the current estimates of the random effects for the first 6 
of the 103 subjects.  The purpose of "verbose" is NOT to dump everything
but only enough to help you evaluate whether the algorithm seems to be

	  hope this helps.
	  Spencer Graves

Greg Distiller wrote:
> Hi
> I have found some postings referring to the fact that one can try and 
> understand why a particular model is failing to solve/converge from the 
> verbose output one can generate when fitting a nonlinear mixed model. I am 
> trying to understand this output and have not been able to find out much:
> **Iteration 1
> LME step: Loglik: -237.4517 , nlm iterations: 22
> reStruct  parameters:
>   subjectno1   subjectno2   subjectno3   subjectno4   subjectno5 
> subjectno6
>  -0.87239181   2.75772772  -0.72892919 -10.36636391   0.55290322 
> 0.09878685
> PNLS step: RSS =  60.50164
>  fixed effects:2.59129  0.00741764  0.57155
>  iterations: 7
> Convergence:
>    fixed reStruct
> 5.740688 2.159285
> I know that the Loglik must refer to the value of the log likelihood 
> function, that the values after "fixed effects" are the parameter estimates, 
> and that the bit after Convergence obviously has something to so with the 
> convergence criteria for the fixed effects and the random effects structure. 
> I did manage to find a posting where somebody said that the restruct 
> parameter is the log of the relative precision of the random effects? The 
> one thing that is a bit confusing to me is that it appears as if the fixed 
> effects convergence must be zero (or close to it) as one would expect but in 
> one of my converged models the output showed a restruct value of 0.72 ?
> Then I have no idea what the numbers under subjectno1-6 are, especially as I 
> have 103 subjects in the data!
> Can anyone help shed some light on this output and how it can be used to 
> diagnose issues with a model?
> Many thanks
> Greg
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