> 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.

> glm(categorical_FOD_FODs ~ Condition_FODs + Language_used_FODs + Condition_FODs:Language_used_FODs, data=indvar_FODs, family=multinomial(link="logit"))
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred 

Call:  glm(formula = categorical_FOD_FODs ~ Condition_FODs + Language_used_FODs +      Condition_FODs:Language_used_FODs, family = multinomial(link = "logit"),      data = indvar_FODs)

Coefficients:
                                                                                        (Intercept)
  ascending                                                                                     -0.002
  descending                                                                                     1.511
  identity                                                                                       3.368
  Condition_FODsB                                                                                0.079
  Condition_FODsC                                                                                0.243
  Condition_FODsD