> 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 categorical_FOD_FODs is the dependent variable. please apply multinomial logistic regression with interaction effect Treat each level of the Condition_FODs variable (4 levels) and Language_used_FODs variable (5 levels) equally. include the interaction effect of variables in the analysis.

library(nnet)
set.seed(1234)

# split dataset into train and test
train <- sample(1:nrow(indvar_FODs), nrow(indvar_FODs)*.7) 
test <- -train

# this function will return a predictions where the sum of probabilities of all 
# possible values of the categorical_FOD_FODs is equal to 1.
mn_logit_model <- multinom(categorical_FOD_FODs ~ Condition_FODs*Language_used_FODs,
                           data = indvar_FODs[train,], maxit = 50)

# prediction
mn_logit_model_pred <- predict(mn_logit_model, indvar_FODs[test,])

# accuracy
mean(max.col(mn_logit_model_pred)==max.col(indvar_FODs[test,]))