Hello,
First of all, I'd like to apologize for my poor English writing.I'm
working with taper equations for predicting stem form of slash pine in
southern Brazil, with a relatively small dataset (n=1560).The 'nlme'
package is the only way, I believe, I could get the results I want.
However I'm having convergence problems with the models. I do realize
that these are complex models, having from 5 up to 9 coefficients,
I've tried to converge with the nlme.control default parameters, but
that's the code I've got after reading this :
http://tolstoy.newcastle.edu.au/R/help/04/07/1278.html
Here is the example for one of the most simple models
(Figueiredo-Filho et al., 1996):
>pol5=lm( MDL2$DIDAP~MDL2$HIH+MDL2$HIH2+MDL2$HIH3+MDL2$HIH4+MDL2$HIH5)> stpol5m=coef(pol5)>.datap <- groupedData(DICC ~ DAP | Arv, data=MDL2,order.groups=T)> pol5m=nlme(DIDAP~B0+B1*HIH+B2*HIH2+B3*HIH3+B4*HIH4+B5*HIH5,           data=datap,method='REML',            fixed=B0+B1+B2+B3+B4+B5 ~1,            start=stpol5m,verbose=TRUE,            control=list(maxIter=1000,niterEM=100,pnlsMaxIter=10,msMaxIter=500))>.pol5mf=update(pol5m,corr=corAR1())

I do understand that this is a linear model (all the other models I'm
testing are non-linear), but I wanted to keep the example simple. I tried
to converge it through 'lme' procedure but I got no convergence, I guess
because of the lack of 'start' parameters (I was able to converge this
model in the 'lmer' procedure, but as far as I know there is no possibility
of modeling correlation structures in the 'lmer' procedure). In the more
complex models I'm testing I wasn't able to obtain convergence even before
accounting for the correlation between measurements (I use nlsLM to get
initial parameters values). Is there anything wrong with my code?


 I realize that one approach to this problem would be to reduce the number
of random coefficients that account for individual tree variation (Li et
al., 2012). But since individual tree heights and diameters are only
obtainable from each individual tree, I guess that all coefficients (which
are associated to these variables) should be random (Gelman, 2007). I
wonder if there is any problem with my interpretation of the mixed models
theory?


 If I reduce the number of random coefficients, what would be the criteria
to choose them?


 Another solution would be to increase the tolerance convergence criterion
(to about 0.001), but I'm afraid of doing that because I have no knowledge
of the consequences. Is there any problem in doing this?


 Would be methodologically acceptable to limit the number of iterations?


 I also could increase the number of maxIter to an absurd number of
iterations (like a million, even though a thousand iterations is already a
high number) and see If I get convergence. But this would take a lot of
computation resource (and time), and before doing that I would like some
insights into this problem (maybe I just need a bigger computer).


 Figueiredo-Filho, A., Borders, B., Hitch, K., 1996. Taper equations for
Pinus taeda plantations in Southern Brazil. For. Ecol. Manage. 83, 39-46.

Gelman, A., 2007. Data analysis using regression and
multilevel/hierarchical models. Cambridge University Press.

Li, R., Weiskittel, A., Dick, A., 2012. Regional stem taper equations for
eleven conifer species in the Acadian region of North America: development
and assessment. North. J. Appl. For. 29, 5-14.


Thank you.

Thomas Schröder.

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