glfmfit logistic regression overparametrization issue

Hello all,

I have a data-set with two categorical predictors (A and B) and one binary output C; therefore, I decided to use logistic regression.

Predictor A has nine possible variables Predictor B has sixteen possible variables

Each possible variable for each predictor has been used at least six times. There are 2002 observations, which each have a corresponding predictor A, B, and out C value.

Given that there are categorical variables, I have grouped them together using dummy variables. When the dummy variable matrix was input into glmfit, I removed a column of variables from the dummy variable matrix in order to account for the intercept.

Despite this, I still get the following response:

Warning: X is ill conditioned, or the model is overparameterized, and some coefficients are not identifiable. You should use caution in making predictions.

My code is as follows:

    data=csvread('Iraq_Afghanistan_DATA_numeric_only.csv');
    target = data(:,4);
    perpetrator = data(:,5);
    weapon= data(:,6);
    tp=[target perpetrator];
    ntp=dummyvar(tp);
    ntp2=ntp(:,1:24);

    [b,dev,stats] = glmfit(ntp2, nweapon','binomial','logit');

Thank you