Printer-friendly version

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.