# 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

# import the datafile
data <- read.csv('C:/Users/jessicasa/Documents/Courses/Spring_2019/STA_6414/Final_project/data/exp1_FODs.csv')

# dependent variable
y <- as.factor(data$categorical_FOD_FODs)

# independent variables
x1 <- data$Condition_FODs
x2 <- data$Language_used_FODs

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

# print model
summary(mod_FODs)