[R-sig-ME] boundary (singular) fit and failure to converge

[Rafael Lima Oliveira]

Dear mailing list at R-sig-mixed-models,



I have a dataset of environmental variables (salinity, temperature, pH,
dissolved oxygen, turbidity and depth) as predictors and as response
variables fish species richness.



Initially, I ran a Poisson-GLMM and this is my model:



poissonmodel<- glmer(Richness~ Salinity*Locality + Temperature*Locality +
pH*Locality + DO*Locality + Turbidity*Locality + Depth*Locality + (1
|Month) + (1|Locality/Site), family="poisson", data = dados)



Warning message:

In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :

  Model failed to converge with max|grad| = 0.00485978 (tol = 0.002,
component 1)



The random-effect variances being estimated as zero (or close to 0)
indicates that my model should be simplified? Should I remove the random
effects terms without substantial loss of fidelity to the data?



The Random structure that I created in my model account for my nested
design, where: (1 |Month) account for temporal variability of my dataset
collected monthly during 1 year and (1|Locality/Site) where indicated that,
spatially my data are nested, in this case "Site" nested in "Locality".



Overdispersion test to Poisson model indicated a dispersion rate of 1.632.
Once overdispersion is detected, I’m refitting my model with negative
binomial distribution (function “glmer.nb) and variance of the random
effects also was close to 0.



*This is my negative binomial model:*



binomialmodel<- glmer.nb(Richness~ Locality + Salinity*Locality +
Temperature*Locality + pH*Locality + DO*Locality + Turbidity*Locality +
Depth*Locality + (1 |Month) + (1|Locality/Site), data = dados)



After run my model some warnings appeared:



*- boundary (singular) fit: see help('isSingular')*



and two warnings about Convergence:



*1:* In optTheta(g1, interval = interval, tol = tol, verbose = verbose,
failure to converge in 10000 evaluations

*2:* In optTheta(g1, interval = interval, tol = tol, verbose = verbose,  :
convergence code 4 from Nelder_Mead: failure to converge in 10000
evaluations



The random effects that I specified in my model are not really needed? If I
remove the random effects, can I change the GLMM approach to GLM approach?



If anybody has any tips on this please give me a hand.

Hope you can understand it.



With many thanks and kind regards,



Rafael

-- 
*Rafael Lima Oliveira*
Doutorando em Biologia Animal
Universidade Federal do Espírito Santo - UFES
Laboratório de Ecologia de peixes marinhos - CEUNES/UFES
*Contato:* (75) 98873-1548 / (27) 99526-3612
*E-mail alternativo:* rafael.l.oliveira using edu.ufes.br
*Currículo Lattes*:  http://lattes.cnpq.br/5215941704013482

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