[R-sig-ME] R^2 for linear mixed effects models with glmer()

[ASANTOS]

Dear Mix Models Members,

        I try to extract R^2 for linear mixed effects models with 
glmer() function with poisson distribution using r.squaredGLMM() in 
MuMIn package, but doesn't work. My output always show:

#Model ajusted > mT <-glmer(riqueza ~tipo_trat+(1|Ponto),data=d1, + 
family=poisson, control = glmerControl(check.conv.singular = 
"warning",optCtrl = list(maxfun=100000))) Warning messages: 1: In 
checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :Model 
failed to converge with max|grad| = 0.00894145 (tol = 0.001, component 
1) 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = 
control$checkConv, :singular fit 3: In checkConv(attr(opt, "derivs"), 
opt$par, ctrl = control$checkConv, :Model is nearly unidentifiable: very 
large eigenvalue - Rescale variables?;Model is nearly unidentifiable: 
large eigenvalue ratio - Rescale variables? #R^2 conditional and 
marginal > r.squaredGLMM(mT) Error in glmer(formula = riqueza ~ 
tipo_trat + temp_final + temp_inici + : fitting model with the 
observation-level random effect term failed. Add the term manually In 
addition: Warning message: In value[[3L]](cond) :(p <- ncol(X)) == 
ncol(Y) is not TRUE

      I change almost all parameters indicating by web posts like 
glmerControl, maxfun, etc. There are other approaches to calculate the 
conditional and marginal R^2 for my model with lme4 package?

Thanks in advance,

Alexandre

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Alexandre dos Santos
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