# Whether to use multinomial logistic regression with a three-category outcome variable and many binary predictors?

I have a dependent variable made up of 3 categories and 14 binary predictor variables.

I have tried using mlogit and nnet/multinom packages in R.

Is there a better approach than multinomial logistic regression for this particular scenario?

• how many data points do you have? also you may have separation if the implicit contingency table has 0 or 100% observed counts. this leads to infinite mles and ill conditioning in the newton rhapson scheme – probabilityislogic Jun 29 '12 at 8:31
• @probabilityislogic - I have a few hundred data points. If I understand your contingency table comment, I think this possible issue was noted as having been checked in my original post. – Aengus Jun 30 '12 at 22:05

• This is not an answer to the question; it is a new question. However, it cannot be answered well in comments. You should post it as a new question and let us help you properly. For the record, it is possible to have ($p$) orthogonal binary predictor variables, so long as $N>2^p$. – gung - Reinstate Monica Jun 29 '12 at 13:36