[R] convergence error code in mixed effects models

[Ilona Leyer]

Here an simple example:

rep	treat	heightfra	leaffra	leafvim	week
ID1	pHf	1.54	4	4	4
ID2	pHf	1.49	4	4	4
ID3	pHf	1.57	4	5	4
ID4	pHf	1.48	4	4	4
ID5	pHf	1.57	4	4	4
ID6	pHs	1.29	4	5	4
ID7	pHs	0.97	4	5	4
ID8	pHs	2.06	4	4	4
ID9	pHs	0.88	4	4	4
ID10	pHs	1.47	4	4	4
ID1	pHf	3.53	5	6	6
ID2	pHf	4.08	6	6	6
ID3	pHf	3.89	6	6	6
ID4	pHf	3.78	5	6	6
ID5	pHf	3.92	6	6	6
ID6	pHs	2.76	5	5	6
ID7	pHs	3.31	6	7	6
ID8	pHs	4.46	6	7	6
ID9	pHs	2.19	5	5	6
ID10	pHs	3.83	5	5	6
ID1	pHf	5.07	7	7	9
ID2	pHf	6.42	7	8	9
ID3	pHf	5.43	6	8	9
ID4	pHf	6.83	6	8	9
ID5	pHf	6.26	6	8	9
ID6	pHs	4.57	6	9	9
ID7	pHs	5.05	6	7	9
ID8	pHs	6.27	6	8	9
ID9	pHs	3.37	5	7	9
ID10	pHs	5.38	6	8	9
ID1	pHf	5.58	7	9	12
ID2	pHf	7.43	8	9	12
ID3	pHf	6.18	8	10	12
ID4	pHf	6.91	7	10	12
ID5	pHf	6.78	7	10	12
ID6	pHs	4.99	6	13	12
ID7	pHs	5.50	7	8	12
ID8	pHs	6.56	7	10	12
ID9	pHs	3.72	6	10	12
ID10	pHs	5.94	6	10	12


I used the procedure described in Crawley´s new R
Book.
For two of the tree response variables
(heightfra,leaffra) it doesn´t work, while it worked
with leafvim (but in another R session, yesterday,
leaffra worked as well...).

Here the commands:

> attach(test)
> names(test)
[1] "week"      "rep"       "treat"     "heightfra"
"leaffra"   "leafvim"  
> library(nlme)
>
test<-groupedData(heightfra~week|rep,outer=~treat,test)
> model1<-lme(heightfra~treat,random=~week|rep)
Error in lme.formula(heightfra ~ treat, random = ~week
| rep) : 
        nlminb problem, convergence error code = 1;
message = iteration limit reached without convergence
(9)

>
test<-groupedData(leaffra~week|rep,outer=~treat,test)
> model2<-lme(leaffra~treat,random=~week|rep)
Error in lme.formula(leaffra ~ treat, random = ~week |
rep) : 
        nlminb problem, convergence error code = 1;
message = iteration limit reached without convergence
(9)

>
test<-groupedData(leafvim~week|rep,outer=~treat,test)
> model3<-lme(leafvim~treat,random=~week|rep)
> summary(model)
Error in summary(model) : object "model" not found
> summary(model3)
Linear mixed-effects model fit by REML
 Data: NULL 
       AIC      BIC    logLik
  129.6743 139.4999 -58.83717

Random effects:
 Formula: ~week | rep
 Structure: General positive-definite, Log-Cholesky
parametrization
            StdDev    Corr  
(Intercept) 4.4110478 (Intr)
week        0.7057311 -0.999
Residual    0.5976143       

Fixed effects: leafvim ~ treat 
               Value Std.Error DF  t-value p-value
(Intercept) 5.924659 0.1653596 30 35.82893  0.0000
treatpHs    0.063704 0.2338538  8  0.27241  0.7922
 Correlation: 
         (Intr)
treatpHs -0.707

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3       
 Max 
-1.34714254 -0.53042878 -0.01769195  0.40644540 
2.29301560 

Number of Observations: 40
Number of Groups: 10 

Is there a solution for this problem?

Thanks!!!

Ilona

--- Douglas Bates <bates at stat.wisc.edu> schrieb:

> On Dec 13, 2007 4:15 PM, Ilona Leyer
> <ileyer at yahoo.de> wrote:
> > Dear All,
> > I want to analyse treatment effects with time
> series
> > data:  I measured e.g. leaf number (five replicate
> > plants) in relation to two soil pH - after 2,4,6,8
> > weeks. I used mixed effects models, but some
> analyses
> > didn´t work. It seems for me as if this is a
> randomly
> > occurring problem since sometimes the same model
> works
> > sometimes not.
> >
> > An example:
> > > names(test)
> > [1] "rep"    "treat"  "leaf"   "week"
> > > library (lattice)
> > > library (nlme)
> > >
> test<-groupedData(leaf~week|rep,outer=~treat,test)
> > > model<-lme(leaf~treat,random=~leaf|rep)
> > Error in lme.formula(leaf~ treat, random =
> ~week|rep)
> 
> Really!? You gave lme a model with random = ~ leaf |
> rep (and no data
> specification) and it tried to fit a model with
> random = ~ week | rep?
> Are you sure that is an exact transcript?
> 
> > :
> >         nlminb problem, convergence error code =
> 1;
> > message = iteration limit reached without
> convergence
> > (9)
> 
> > Has anybody an idea to solve this problem?
> 
> Oh, I have lots of ideas but without a reproducible
> example I can't
> hope to decide what might be the problem.
> 
> It appears that the model may be over-parameterized.
>  Assuming that
> there are 4 different values of week then ~ week |
> rep requires
> fitting 10 variance-covariance parameters. That's a
> lot.
> The error code indicates that the optimizer is
> taking
>