> 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$Language_used_FODs) English German Hungarian Italian Turkish 880 160 160 160 2160 categorical_FOD_FODs is the dependent variable. how can I apply multinomial logistic regression with interaction effect Treat each level of the Condition_FODs variable (4 levels) and Language_used_FODs variable (5 levels) equally. include the interaction effect of variables in the analysis.
indvar_FODs<-read.csv(file="indvar_FODs.csv", header=TRUE, sep=",") head(indvar_FODs) #transforming categorical_FOD_FODs variable to factor variable indvar_FODs$categorical_FOD_FODs<-as.factor(indvar_FODs$categorical_FOD_FODs) #Treating categorical_FOD_FODs variable as dependent variable mydata<-indvar_FODs[,c("categorical_FOD_FODs","Condition_FODs","Language_used_FODs","alltogether_FODs","sentence_FODs","verb_FODs","num_FODs")] #logistic regression logistic_regression = glm(categorical_FOD_FODs ~ Condition_FODs * Language_used_FODs * alltogether_FODs * sentence_FODs * verb_FODs *
