I was not expecting so large numbers when I passed that advice. In general
the preliminary EM step are "slower" in comparison with BFGS.
Number of plots in itself should not be the issue in itself - broadly
speaking.
It is the fact that your n is 500k that worries me. That's more like lme4
territory. You may want to consider using lmer() and examining if just
setting some ‘prior weights’ is adequate to account for possible
heteroscedasticity in your sample.



On Tue, Apr 30, 2013 at 7:09 PM, Boulanger, Yan <
Yan.Boulanger@rncan-nrcan.gc.ca> wrote:

> **
> Thanks! I hope this will  converge today as I have more than 31 000 plots
> and n ~ 500 000.
>
>
> *Yan Boulanger, Postdoctoral Visiting Fellow*
> Ressources Naturelles Canada, Canadian Forest Service
> Centre de Foresterie des Laurentides
> 1055, rue du P.E.P.S.
> C.P. 10380, succ. Sainte-Foy
> Québec (Québec) Canada
> G1V 4C7
> Tel. : +1 418 649-6859
>
>
>  ------------------------------
> *From:* Pantelis Hadjipantelis [mailto:kalakouentin@gmail.com]
> *Sent:* 30 avril 2013 14:01
> *To:* Boulanger, Yan
> *Cc:* r-sig-mixed-models@r-project.org
> *Subject:* Re: [R-sig-ME] Convergence problem
>
>   Regardless of possible nlme-syntax / experimental design issues.
>
> Optimization-wise, you might want to increase (substantially) the EM
> iterations the solver (under the niterEM argument in lmeControl) uses to
> refine the initial solution of the random effects variance-covariance
> coefficients. This is not a panacea but it might prove useful in cases of
> slow convergence
> Also for inspection purposes, set msVerbose  = T, so you can see how the
> BFGS is doing during optimization.
>
>
>
> On Tue, Apr 30, 2013 at 6:21 PM, Boulanger, Yan <
> Yan.Boulanger@rncan-nrcan.gc.ca> wrote:
>
>> Hi folks,
>>
>> I'm new to nlme and I'm trying to fit a somewhat simple mixed regression
>> model:
>>
>> PICE.MAR_tree_dbh_model <- lme(log(dbh_tree) ~
>> Temp+Prec+I(Prec/age_tree)+I(Temp/age_tree), weights = varPower(form
>> =~fitted(.)) , random= ~1|plot_name, data=PICE.MAR_tree, method="REML")
>>
>>
>> However, convergence is never reached:
>>
>>   (Error in lme.formula(log(dbh_tree) ~ Temp + Prec + I(Prec/age_tree) +
>>  :
>>   maximum number of iterations (lmeControl(maxIter)) reached without
>> convergence)
>>
>>  Here's the structure of my data. dbh_tree and age_tree is measured on
>> the tree which is nested in plot_name (random effect). Temp and Prec are
>> measured at the plot level. I have also a very unbalanced setup with
>> sometimes only 1 tree per plot and others where there are as much as 150
>> trees. Maybe the syntax is not right.
>>
>> Thank you!
>>
>> Yan Boulanger
>>
>>
>>
>>
>> >Yan Boulanger, Postdoctoral Visiting Fellow
>> >Ressources Naturelles Canada, Canadian Forest Service
>> >Centre de Foresterie des Laurentides
>> >1055, rue du P.E.P.S.
>> >C.P. 10380, succ. Sainte-Foy
>> >Québec (Québec) Canada
>> >G1V 4C7
>> >Tel. : +1 418 649-6859
>>
>> _______________________________________________
>> R-sig-mixed-models@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
>

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