> table(indvar_FODs$categorical_FOD_FODs) ascending descending identity 1683 1432 206 > table(indvar_FODs$Condition_FODs) A B C D 880 880 880 880 > table(indvar_FODs$Language_used_FODs) English German Hungarian Italian Turkish 880 160 160 160 2160 # dependent variable y <- as.factor(indvar_FODs$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') > mod_FODs_optimizer <- glmer(y ~ x1 * x2 + (1|subject_FODs), family = binomial, data = indvar_FODs, + control = glmerControl(optimizer = "bobyqa", + optCtrl=list(method='nlminb', maxfun=6000000), calc.derivs = FALSE)) Warning messages: 1: In (function (npt = min(n + 2L, 2L * n), rhobeg = NA, rhoend = NA, : unused control arguments ignored 2: In (function (npt = min(n + 2L, 2L * n), rhobeg = NA, rhoend = NA, : unused control arguments ignored > Edit this according to the data

`# 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)`