For discriminant analysis all the predictors are not used. Only the continuous variables and the ordinal variables are used as for the nominal variables there will be no concept of group means and linear discriminants will be difficult to interpret. The predictors are assumed to have a multivariate normal distribution.
Sample R code for Discriminant Analysis
library(MASS) ldafit <- lda(Creditability ~ Value.Savings.Stocks + Length.of.current.employment + Duration.of.Credit..month.+ Credit.Amount + Age..years., data = Train50) ldafit plot(ldafit) lda.pred <- predict(ldafit, data=Test50) ldaclass <- lda.pred$class table(ldaclass, Test50$Creditability) qdafit <- qda(Creditability ~ Value.Savings.Stocks + Length.of.current.employment + Duration.of.Credit..month.+ Credit.Amount + Age..years., data = Train50) qdafit qda.pred <- predict(qdafit, data=Test50) qdaclass <- qda.pred$class table(qdaclass, Test50$Creditability)
Prior probability was taken as observed in the Training sample:
71.4% Creditable and 28.6% Non-creditable
Linear Discriminant Analysis
Quadratic Discriminant Analysis
Neither logistic regression nor discriminant analysis is performing well for this data. The reason DA may not do well is that, most of the predictors are categorical and nominal predictors are not used in this analysis.