Re: [R] understanding the verbose output in nlme

From: Greg Distiller <>
Date: Mon 05 Jun 2006 - 17:20:48 EST

Thanks for the reply...will give some thought to your suggestion about stepping through the function.
I have read the Pinheiro and Bates book, in fact its my primary reference for getting into the nonlinear mixed models with R. Lastly wrt the bit under subjects 1-6, I had thought about it being an estimated random effect but in this model there are 2 random effects so not sure if that holds...
thanks again...

> 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
> converging.
> 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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> mailing list PLEASE do read the posting guide! Received on Mon Jun 05 17:43:02 2006

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