[R-sig-ME] Singular convergence in lmer

[Benjamin J. Ciotti]

Dear Thierry,

Thank you for your response. If you look carefully at the data structure and
M1, fish is in fact a random effect (residual error variance) in the
original model. I considered including year as a fixed effect for the reason
you mentioned, but others have suggested that the number of levels shouldn't
influence whether a factor is fixed or random. Maybe I should reconsider?
Site actually has 7 levels in the 'real' dataset - enough to estimate
variance according to Zuur et al.'s criteria.

Thanks again for your input.

Ben

-----Original Message-----
From: ONKELINX, Thierry [mailto:Thierry.ONKELINX at inbo.be] 
Sent: Monday, October 03, 2011 5:52 AM
To: Benjamin J. Ciotti; r-sig-mixed-models at r-project.org
Subject: RE: [R-sig-ME] Singular convergence in lmer

Dear Benjamin,

I think you will need to do some reading on mixed models. I would suggest
Zuur et al (2009)

@BOOK{
  title = {Mixed Effects Models and Extensions in Ecology with R},
  publisher = {Springer New York},
  year = {2009},
  author = {Zuur, Alain F. and Ieno, Elena N. and Walker, Neil J. and
Saveliev,
	Anatoly A. and Smith, Graham M.},
  doi = {10.1007/978-0-387-87458-6},
}

I would expect to see fish as a random effect in your model. Further you
have too few levels of year and site to get a reliable estimate of the
variance. Hence a more sensible model would be something like:

lmer(G~ Day * Year * Site + (1|Fish),REML=TRUE,data=GData)

Best regards,

Thierry


> -----Oorspronkelijk bericht-----
> Van: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-
> bounces at r-project.org] Namens Benjamin J. Ciotti
> Verzonden: maandag 3 oktober 2011 1:02
> Aan: r-sig-mixed-models at r-project.org
> Onderwerp: [R-sig-ME] Singular convergence in lmer
> 
> #Dear All,
> 
> 
> 
> #I have measured growth rate (G, response variable) in 10 individual fish
> (Fish) on 5 dates (Days, fixed covariate) at 4 sites (Site) in each of 2
years (Year).
> 
> #An example data set, using random numbers for the response, is GData, as
> follows:
> 
> 
> 
> set.seed(14)
> 
> Year<-as.factor(sort(rep(c(2005,2007),400)))
> 
> Site<-as.factor(rep(sort(rep(c("a","b","c","d"),50)),4))
> 
> Year.Site<-as.factor(paste(Year,Site,sep="."))
> 
> Day<-as.numeric(rep(sort(rep(c(1,15,30,45,60),10)),16))
> 
> Fish<-as.factor(rep(seq(1,10),80))
> 
> G<-rnorm(800,0.05,0.025)
> 
> GData<-data.frame(Year,Site,Year.Site,Day,Fish,G)
> 
> #I am modelling the temporal trend in growth rate as a linear function of
Day,
> the slope and intercept of which can vary as a random function of Year,
Site and
> Year.Site.
> 
> library(lme4)
> 
> M1<-
> lmer(G~Day+(1|Year)+(1|Site)+(0+Day|Year)+(1|Year.Site)+(0+Day|Site)+(0+
> Day|Year.Site),REML=TRUE,data=GData)
> 
> 
> 
> #You will note that there is singular convergence (with this, but not all,
random
> number seeds).
> 
> #Does this necessarily mean that there is a fundamental flaw in the model
> design, or does it just mean that with this data set, a simpler model is
necessary?
> 
> 
> 
> #In the real data set, all random terms except Year.Site and the
interaction
> between Day and Year.Site can be dropped without substantial changes in
AIC.
> 
> #I therefore conclude that the following model is sufficient to describe
the
> spatial and temporal growth variation:
> 
> M2<-lmer(G~Day+(1|Year.Site)+(0+Day|Year.Site),REML=TRUE,data=GData)
> 
> 
> 
> #My question is whether the initially model is fundamentally flawed, or
whether
> it just needs to be simplified?
> 
> #For example, is it valid to compare the full model with simpler models,
even
> though it converged to singularity?
> 
> 
> 
> #Any advice would be greatly appreciated.
> 
> 
> 
> Benjamin J. Ciotti
> 
> University of Delaware
> 
> 
> 
> 
> 	[[alternative HTML version deleted]]
> 
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models