I was trying to fit a GLMM with a binomial distribution (for Yes/No data) in R, and kept running into convergence warnings, which seemed founded given the similar SE's and p-values for the different predictors in the model. After a bit of trial-and-error, I was able to fit this model by specifying BOBYQA as the optimizer for both parts and increasing the maximum number of iterations to 1000.

Example:

glmer(DCyn ~ Hc + Tc + Cc + Mc + (1|ID), data=data,
 control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=2e4)))

However, a colleague suggested that I should try running the model with a quasi-binomial distribution. I had no problems fitting my original model with a quasi-binomial distribution (i.e., without change anything in control), so now I'm wondering which model is most appropriate for the data.

Normally, I would compare AICs between the two models, but I'm unsure how to do this with a quasi-binomial model (and whether the qAIC is comparable with the AIC from the binomial model). Any thoughts? Or is there a better way to compare these models?

I was trying to fit a GLMM with a binomial distribution (for Yes/No data) in R, and kept running into convergence warnings, which seemed founded given the similar SE's and p-values for the different predictors in the model. After a bit of trial-and-error, I was able to fit this model by specifying BOBYQA as the optimizer for both parts and increasing the maximum number of iterations to 1000.

Example:

glmer(DCyn ~ Hc + Tc + Cc + Mc + (1|ID), 
    data=data, control = glmerControl(optimizer 
    = "bobyqa", 
    optCtrl = list(maxfun=2e4)))

However, a colleague suggested that I should try running the model with a quasi-binomial distribution. I had no problems fitting my original model with a quasi-binomial distribution (i.e., without change anything in control), so now I'm wondering which model is most appropriate for the data.

Normally, I would compare AICs between the two models, but I'm unsure how to do this with a quasi-binomial model (and whether the qAIC is comparable with the AIC from the binomial model). Any thoughts? Or is there a better way to compare these models?

I was trying to fit a glmm with a binomial distribution (for Yes/No data) in R, and kept running into convergence warnings, which seemed founded given the similar SE's and p-values for the different predictors in the model. After a bit of trial-and-error, I was able to fit this model by specifying bobyqa as the optimizer for both parts and increasing the maximum number of iterations to 1000.

Example:

glmer(DCyn ~ Hc + Tc + Cc + Mc + (1|ID), data=data,
 control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=2e4)))

However, a colleague suggested that I should try running the model with a quasi-binomial distribution. I had no problems fitting my original model with a quasi-binomial distribution (i.e., without change anything in control), so now I'm wondering which model is most appropriate for the data.

Normally, I would compare AIC's between the two models, but I'm unsure how to do this with a quasi-binomial model (and whether the qAIC is comparable with the AIC from the binomial model). Any thoughts? Or is there a better way to compare these models?

I was trying to fit a GLMM with a binomial distribution (for Yes/No data) in R, and kept running into convergence warnings, which seemed founded given the similar SE's and p-values for the different predictors in the model. After a bit of trial-and-error, I was able to fit this model by specifying BOBYQA as the optimizer for both parts and increasing the maximum number of iterations to 1000.

Example:

glmer(DCyn ~ Hc + Tc + Cc + Mc + (1|ID), data=data,
 control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=2e4)))

However, a colleague suggested that I should try running the model with a quasi-binomial distribution. I had no problems fitting my original model with a quasi-binomial distribution (i.e., without change anything in control), so now I'm wondering which model is most appropriate for the data.

Normally, I would compare AICs between the two models, but I'm unsure how to do this with a quasi-binomial model (and whether the qAIC is comparable with the AIC from the binomial model). Any thoughts? Or is there a better way to compare these models?

I was trying to fit a glmm with a binomial distribution (for Yes/No data) in R, and kept running into convergence warnings, which seemed founded given the similar SE's and p-values for the different predictors in the model. After a bit of trial-and-error, I was able to fit this model by specifying bobyqa as the optimizer for both parts and increasing the maximum number of iterations to 1000.

Example:

glmer(DCyn~Hc+Tc+Cc+Mc+(1|ID), data=data,
 control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=2e4)

However, a colleague suggested that I should try running the model with a quasi-binomial distribution. I had no problems fitting my original model with a quasi-binomial distribution (i.e., without change anything in control), so now I'm wondering which model is most appropriate for the data.

Normally, I would compare AIC's between the two models, but I'm unsure how to do this with a quasi-binomial model (and whether the qAIC is comparable with the AIC from the binomial model). Any thoughts? Or is there a better way to compare these models?

I was trying to fit a glmm with a binomial distribution (for Yes/No data) in R, and kept running into convergence warnings, which seemed founded given the similar SE's and p-values for the different predictors in the model. After a bit of trial-and-error, I was able to fit this model by specifying bobyqa as the optimizer for both parts and increasing the maximum number of iterations to 1000.

Example:

glmer(DCyn ~ Hc + Tc + Cc + Mc + (1|ID), data=data,
 control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=2e4)))

However, a colleague suggested that I should try running the model with a quasi-binomial distribution. I had no problems fitting my original model with a quasi-binomial distribution (i.e., without change anything in control), so now I'm wondering which model is most appropriate for the data.

Normally, I would compare AIC's between the two models, but I'm unsure how to do this with a quasi-binomial model (and whether the qAIC is comparable with the AIC from the binomial model). Any thoughts? Or is there a better way to compare these models?