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

ASANTOS alexandresantosbr at yahoo.com.br
Mon Feb 5 20:15:20 CET 2018

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 dos Santos
Proteção Florestal
IFMT - Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso
Campus Cáceres
Caixa Postal 244
Avenida dos Ramires, s/n
Bairro: Distrito Industrial
Cáceres - MT                      CEP: 78.200-000
Fone: (+55) 65 99686-6970 (VIVO) (+55) 65 3221-2674 (FIXO)

         alexandre.santos at cas.ifmt.edu.br  
OrcID: orcid.org/0000-0001-8232-6722
LinkedIn: br.linkedin.com/in/alexandre-dos-santos-87961635

	[[alternative HTML version deleted]]

More information about the R-sig-mixed-models mailing list