Error: In mer_finalize(ans) : false convergence (8)

The interaction() is a fancy way of creating a unique ID for each subplot. I presume that each subplot gets the same TMT1 and TMT2 over time.

It would have been beneficial to consult a statistician before starting the experiment. The sample size is too small for the design or the design is too complex for the sample size. This issue can be addressed prior to starting the experiment by simplifying the design (e.g. drop either TMT1 or TMT2), increasing the sample size or both.

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 at inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey


-----Oorspronkelijk bericht-----
Van: Sarah Dryhurst [mailto:s.dryhurst at gmail.com]
Verzonden: vrijdag 26 juli 2013 13:57
Aan: ONKELINX, Thierry
CC: r-sig-mixed-models at r-project.org
Onderwerp: Re: [R-sig-ME] Error: In mer_finalize(ans) : false convergence (8)

Hi Thierry,

Sorry about that.  Can I ask you to explain to be the reason behind the specification of the interaction for "SubplotID"?

The number of parameter estimates is indeed an issue.  The experiment will actually be running for a few more years so that I can get plenty more data, but unfortunately for the purpose of my thesis I have to write up what i've got so far...  I have some baseline 2010 data that I may be able to include also though, as a fourth year.

Thanks again for your help!

Sarah

On Fri, Jul 26, 2013 at 12:35 PM, ONKELINX, Thierry <Thierry.ONKELINX at inbo.be> wrote:

Sarah,

Always keep the mailing list in cc while replying.

After having a close look to you design I would suggest to go for one of the models below. If you are interested how the effect of TMT1 and TMT2 change in time go for Mod1. Go for Mod2 is you expect that their effect remains constant over time. Otherwise go for Mod3.

lmerdat$SpuplotID <- with(lmerdat, interaction(Block, TMT1, TMT2))
Mod1 <- lmer(log(Y) ~ TMT1 * TMT2 * Year + (1|Block/SubplotID), data =
lmerdat, verbose = TRUE)
Mod2 <- lmer(log(Y) ~ TMT1 * TMT2 + (1|Block/SubplotID) + (1|Year),
data = lmerdat, verbose = TRUE)
Mod3 <- lmer(log(Y) ~ TMT1 * TMT2 + (1|Block/SubplotID) + (0 + TMT1 :
TMT2|Year), data = lmerdat, verbose = TRUE)

Assuming Year is continuous Mod1 requires 10 parameters to be estimated. Your design yields 24 observations per year. Unless you have at least 4 years of data, the model is too complex for your data. You might want to look at the quote of Fisher in my signature...

Best regards,

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 at inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey


-----Oorspronkelijk bericht-----
Van: Sarah Dryhurst [mailto:s.dryhurst at gmail.com]
Verzonden: vrijdag 26 juli 2013 12:07
Aan: ONKELINX, Thierry
Onderwerp: Re: [R-sig-ME] Error: In mer_finalize(ans) : false
convergence (8)

Dear Thierry,

Thanks so much for you reply,  Apologies - I should have explained the experiment better.  I have:

6 blocks
2 plots per block: one receiving one level of TMT1, one as a TMT1
control
2 subplots per plot: one receiving TMT2, one as TMT2 control.

The response is a measure of biomass (production) in each subplot I
tried removing it as a fixed effect as you suggested, and the model
does indeed converge.  However I'm interested in the effects of both
TMT1 and TMT2 and how they might interact, so I want to retain TMT1 as both a fixed effect and a random effect nested with block (due to the nature of the design)...  Perhaps I could remove year as a fixed effect?  Forgive me if I am making a silly mistake here: still learning this stuff!

Thanks again

Sarah

On Fri, Jul 26, 2013 at 9:16 AM, ONKELINX, Thierry <Thierry.ONKELINX at inbo.be> wrote:

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 at inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey


-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] Namens Sarah
Dryhurst
Verzonden: donderdag 25 juli 2013 19:51
Aan: r-sig-mixed-models at 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,verbo
s
e=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|Bl
o
c
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 at imperial.ac.uk
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The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document.

--
NERC PhD Student
Community Ecology and Global Change
Department of Biology
Imperial College, London
email: sarah.dryhurst08 at imperial.ac.uk
* * * * * * * * * * * * * D I S C L A I M E R * * * * * * * * * * * *
* Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer en binden het INBO onder geen enkel beding, zolang dit bericht niet bevestigd is door een geldig ondertekend document.
The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document.

--
NERC PhD Student
Community Ecology and Global Change
Department of Biology
Imperial College, London
email: sarah.dryhurst08 at imperial.ac.uk
* * * * * * * * * * * * * D I S C L A I M E R * * * * * * * * * * * * *
Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer en binden het INBO onder geen enkel beding, zolang dit bericht niet bevestigd is door een geldig ondertekend document.
The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document.