R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> invisible(options(echo = TRUE))
> library(stats)
> source("psopts.r")
> train <- read.table("loans.dat",header=F,col.names=c("x1","x2","y"))
> 
> x1 <- train$x1
> x2 <- train$x2
> y  <- train$y
> y0 <- (y==1)
> 
> fit <- lsfit(cbind(x1,x2),y,intercept=TRUE)
> 
> b <- as.numeric(fit$coef)
> yhat <- y - fit$residuals
> idx  <- order(yhat)
> yhat0 <- (yhat > 0.5)
> err <- abs(y-yhat0);
> print(sum(err));
[1] 17
> 
> gistic <- glm(y ~ x1 + x2, family=binomial,data=train)
> lhat <- gistic$fitted.values
> lhat0 <- (lhat > 0.5)
> err <- abs(y-lhat0);
> print(sum(err));
[1] 16
> 
> disagree = (yhat0!=lhat0)
> 
> postscript(file="score.eps")
> 
> plot(x=yhat,y=yhat,type='n',
+   xlab="Linear Regression Score",ylab="Score")
> points(x=yhat,y=y,pch='o',col=3-y) # red = 2, green = 3
> lines(x=c(min(yhat),max(yhat)),y=c(0.5,0.5))
> lines(y=c(min(yhat),max(yhat)),x=c(0.5,0.5))
> lines(x=yhat[idx],y=yhat[idx])
> points(x=yhat[idx],y=lhat[idx],pch='.',col="black",cex=3)
> points(x=yhat[idx],y=lhat[idx],pch='o',col=4*disagree[idx])
> text(y=0.00,x=0.80,"<- errors")
> text(y=1.00,x=0.18,"errors ->")
> text(y=0.42,x=0.70,"<- disagree")
> 
> dev.off()
null device 
          1 
> 
> ryhat <- rank(yhat)
> rlhat <- rank(lhat)
> 
> print(cor(ryhat,rlhat))
[1] 0.9980416
> 
> 
> proc.time()
[1] 0.96 0.14 2.79 0.00 0.00
>