> 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.

#Load data
data("FOD_data")

#Preprocess data
FOD_data<-FOD_data[,-c(1,3,5)]

#Split data
set.seed(1)
indvar_FODs<-FOD_data[sample(nrow(FOD_data), size = 0.7*nrow(FOD_data), replace = FALSE),]
outvar_FODs<-FOD_data[-which(rownames(FOD_data) %in% rownames(indvar_FODs)),]

#Partial effect plot

#Generalized linear model
FODs_glm<-glm(categorical_FOD_FODs~Condition_FODs*Language_used_FODs, data = indvar_FODs, family = "binomial")
summary(FODs_glm)

#Multinomial logistic regression
FODs_multinomial<-multinom(categorical_FOD_FODs~Condition_FOD