I have a set of Independent Variables - both Categorical Variables and Continuous Variables. There is the predictor variable which have certain classes say C1 to Cn. The aim is to predict the category membership!
I'm facing two issues. Any discriminant procedure requires only continuous variables for prediciting. And second, logistic regression which can be used produces probability values of category membership, which does not equivalently specify the inter-class variance using distance measures like a Canonical Discriminant Analysis does using %plotit macro.
Hence, I've got two questions.
1. If I've got mixed variables - both Continuous & Catergorical, can I still predict membership of category in the predictor variable? If yes, how?
2. If the answer to the above is to use Logistic Regression or Genmod/Catmod, can I still obtain a plot of the various observations that are governed by the category in a distance measure plot to find out the between category variance/distance and hence understand visually what is the scenario of the categories.
Also, I'm not able to plot using %plotit due to the high no. of observations I've got (1.5 Mi). Do I need to consider a downscaling to bring it down to a lesser no? Or can I plot a contour to know the idea of the area coverage?