# dependent variable y <- as.factor(indvar_FODs$categorical_FOD_FODs) # independent variables x1 <- indvar_FODs$Condition_FODs x2 <- indvar_FODs$Language_used_FODs # model with optimizer argument mod_FODs <- glmer(y ~ x1 * x2 + (1|subject_FODs), family = binomial, data = indvar_FODs, control = glmerControl(optimizer = "bobyqa")) # print model summary(mod_FODs) > mod_FODs_optimizer <- glmer(y ~ x1 * x2 + (1|subject_FODs), family = binomial, data = indvar_FODs, + control = glmerControl(optimizer = "bobyqa")) boundary (singular) fit: see help('isSingular') add optCtrl=list including the method' maxfun, and calc.derivs according to the data. improve this test write the edited version of the code

x <- rnorm(10)

# optimized glmer
mod_FODs <- glmer(y ~ x1 * x2 + (1|subject_FODs), family = binomial, data = indvar_FODs,
                  control = glmerControl(optimizer = "bobyqa"))

# print model
summary(mod_FODs)
#> boundary (singular) fit: see help('isSingular')

#> Generalized linear mixed model fit by maximum likelihood
#>   (Laplace Approximation) [glmerMod]
#>  Family: binomial  ( logit )
#> Formula: y ~ x1 * x2 + (1 | Subject_FODs)
#>    Data: indvar_FODs
#> 
#>      AIC      BIC   logLik deviance df.resid 
#>   48.717   56.704  -17.358   34.717       20 
#> 
#> Scaled residuals: 
#>     Min      1Q