[R-sig-ME] Error: In mer_finalize(ans) : false convergence (8)

Fri Jul 26 10:16:03 CEST 2013

Dear Sarah,

Have TMT1 both in the fixed effects and nested in block as a random effect seems a strange to me. This might be the problem for the convergence.

1) What is the rationale for include TMT1 both in the fixed as the random effects?
2) Do you get convergence with
Mod1 <- lmer(log(Y) ~ TMT1 * TMT2 * Year + (1|Block) + (1|Year), data = lmerdat, verbose = TRUE)
Mod2 <- lmer(log(Y) ~ TMT2 * Year + (1|Block/TMT1) + (1|Year), data = lmerdat, verbose = TRUE)

Andpleaseaddsomewhitespacetoyourcode

Best regards,

Thierry

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx op inbo.be
www.inbo.be

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-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces op r-project.org [mailto:r-sig-mixed-models-bounces op r-project.org] Namens Sarah Dryhurst
Verzonden: donderdag 25 juli 2013 19:51
Aan: r-sig-mixed-models op r-project.org
Onderwerp: [R-sig-ME] Error: In mer_finalize(ans) : false convergence (8)

Hello all,

I am running an mixed effects model on the attached data.  I initially used the following code

mod1<- lmer(log(Y)~TMT1*TMT2*Year+(1|Block/TMT1)+(1|Year),data=lmerdat,verbose=TRUE)

Which gives me the following:

  0:     77.714051: 0.666667 0.471405 0.333333
  1:     74.110931: 0.842664  1.45578 0.328587
  2:     72.264680: 0.440773  1.34787 0.215307
  3:     72.215476: 0.405289  1.27443 0.208252
  4:     72.206023: 0.430340  1.25642 0.208492
  5:     72.203289: 0.423498  1.22704 0.214935
  6:     72.202721: 0.422680  1.22886 0.209101
  7:     72.202679: 0.424822  1.23459 0.208315
  8:     72.202679: 0.424822  1.23459 0.208315
  9:     72.202679: 0.424822  1.23459 0.208315
Warning message:
In mer_finalize(ans) : false convergence (8)

I thought that I might have too many parameters, so I removed the three way interaction thus:

mod1<- lmer(logY)~TMT1+TMT2+Year+TMT1:TMT2+TMT1:Year+TMT2:Year+(1|Block/TMT1)+(1|Year),data=lmerdat,verbose=TRUE)

 0:     82.176953: 0.666667 0.471405 0.333333
  1:     80.060839: 0.932569  1.42347 0.182093
  2:     79.408200:  0.00000  1.72496 0.180290
  3:     78.436511: 0.00107482  1.31540 0.182908
  4:     78.313534: 0.00586792  1.19163 0.169823
  5:     78.292542: 0.0255058  1.16060 0.176287
  6:     78.204264: 0.195335 0.918497 0.214946
  7:     77.959243: 0.166960  1.02200 0.173634
  8:     77.325791: 0.416075  1.15366 0.271404
  9:     77.240857: 0.377681  1.16245 0.202179
 10:     77.237968: 0.381277  1.16539 0.190858
 11:     77.237719: 0.380920  1.16760 0.187107
 12:     77.237718: 0.379746  1.17007 0.187519
 13:     77.237695: 0.381074  1.16994 0.187148
 14:     77.237695: 0.380772  1.16969 0.187087
 15:     77.237695: 0.380778  1.16974 0.187092
 16:     77.237695: 0.380806  1.16978 0.187098
 17:     77.237695: 0.380906  1.16978 0.187107
 18:     77.237695: 0.380999  1.16975 0.187128
 19:     77.237695: 0.380982  1.16965 0.187105
 20:     77.237695: 0.380961  1.16967 0.187112
 21:     77.237695: 0.380961  1.16967 0.187112
Warning message:
In mer_finalize(ans) : false convergence (8)
>

So the model seemed to get a bit further but still will not converge...  I don't want to remove anything else as my model will then not invetsigate the relationships I want it to and I don't think it is overparameterised without that 3 way interaction...

If i multiply Y by 10 it gets a bit further again but still stalls:

> mod1<-
> lmer(log(I(10*Y))~TMT1+TMT2+Year+TMT1:TMT2+TMT1:Year+TMT2:Year+(1|Bloc
> k/TMT1)+(1|Year),data=lmerdat,verbose=TRUE)
  0:     82.176953: 0.666667 0.471405 0.333333
  1:     80.059473: 0.932336  1.42350 0.181868
  2:     79.407034:  0.00000  1.72457 0.181162
  3:     78.529865:  0.00000  1.30356 0.0708756
  4:     78.320758: 0.000702149  1.19618 0.193718
  5:     78.308420: 0.00287500  1.16437 0.161311
  6:     78.298595: 0.0199600  1.14541 0.174745
  7:     78.198050: 0.0954940  1.07394 0.224686
  8:     78.095409: 0.159180  1.01562 0.265286
  9:     77.628631: 0.238546  1.05911 0.235037
 10:     77.251998: 0.395868  1.12462 0.185342
 11:     77.239703: 0.373209  1.15530 0.187029
 12:     77.238348: 0.378481  1.15960 0.186789
 13:     77.237962: 0.380487  1.16277 0.186797
 14:     77.237766: 0.380997  1.16609 0.186947
 15:     77.237720: 0.380749  1.16757 0.187021
 16:     77.237698: 0.381015  1.16893 0.187108
 17:     77.237697: 0.380978  1.16905 0.187112
 18:     77.237696: 0.380954  1.16929 0.187109
 19:     77.237696: 0.381002  1.16935 0.187111
 20:     77.237695: 0.381017  1.16942 0.187070
 21:     77.237695: 0.380947  1.16958 0.187109
 22:     77.237695: 0.380947  1.16976 0.187103
 23:     77.237695: 0.380948  1.16975 0.187105
 24:     77.237695: 0.380948  1.16975 0.187105
 25:     77.237695: 0.380948  1.16975 0.187105
 26:     77.237695: 0.380948  1.16975 0.187105
Warning message:
In mer_finalize(ans) : false convergence (8)

Is there a solution to this?  Can I trust the model outputs that I am getting in spite of this convergence issue?  I can't use the suggested resolution of dividing or multiplying explanatory variables by
10/100/1000 etc as these variables are categorical....

Sorry to post another question about this - I just can't find an answer that fits my data.

Any thoughts would be much appreciated

Best wishes,

Sarah

--
NERC PhD Student
Community Ecology and Global Change
Department of Biology
Imperial College, London
email: sarah.dryhurst08 op imperial.ac.uk
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