> 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. how can I 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(car)
library(nnet)
library(MASS)

set.seed(123)
n = length(indvar_FODs$Condition_FODs)

FODs.train <- sample(1:n, 0.7*n)
FODs.test  <- setdiff(1:n, FODs.train)

train.features <- model.matrix(categorical_FOD_FODs ~ -1 + Condition_FODs * Language_used_FODs, data=indvar_FODs)
test.features <- model.matrix(categorical_FOD_FODs ~ -1 + Condition_FODs * Language_used_FODs, data=indvar_FODs)[FODs.test,]

FODs.pred.1 <- multinom(categorical_FOD_FODs ~ Condition_FODs + Language_used_FODs, data=indvar_FODs[FODs.train,])
FODs.pred.