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

[Sarah Dryhurst]

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|Block/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 at imperial.ac.uk