library(MASS) # NOBS=seq(50,800,by=10) Accuracy = vector(mode="numeric", length=length(NOBS)) NFeats=50 # for (n in 1:length(NOBS)){ Class=(runif(n=NOBS[n])>0.5) Feats=as.data.frame(matrix(nrow=NOBS[n],ncol=NFEATS, data=runif(n=NOBS[n]*NFEATS))) model = lda(Class~., data=Feats) predicted = predict(model,Feats) NCorrect = sum(predicted$class == Class) Accuracy[n] = NCorrect/NOBS[n] } # plot(NOBS, Accuracy, col="red", xlab="Number of Observations", ylab="Accuracy", main="Uniform Random Data with 50 Features")