## source("regression.R")

library(Rfwdmv)
data(bank.dat)

x <- bank.dat

x <- data.frame(x,c(rep("genuine",100),rep("forged",100)))
names(x) <- c("length","left.height","right.height",
              "lower.frame","upper.frame","diagonal","group")

## simple linear regression
l <- lm( lower.frame ~ upper.frame, data=x )
print(l)
print(summary(l))
print(anova(l))

l1 <- lm( lower.frame ~ upper.frame, data=x[x$group=="forged",] )
print(l1)
print(summary(l1))
print(anova(l1))

print(cor(x[,1:6]))
print(cor(x[1:100,1:6]))

col <- c(rep("blue",100),rep("red",100))
pch <- c(rep(1,100),rep(8,100))
plot(x$lower.frame,x$upper.frame,col=col,pch=pch)

## multiple linear regression
l <- lm( lower.frame ~ upper.frame + diagonal + length, data=x )
print(l)
print(summary(l))
print(anova(l))

## multiple linear regression with dummies
library(Ecdat)
data(Wages)
l <- lm( lwage ~ ed + exp + sexe , data=Wages)
print(summary(l))

l1 <- lm( lwage ~ ed + exp + sexe + bluecol +south + married, data=Wages)
print(summary(l1))

l2 <- lm( lwage ~ ed + exp + sexe + bluecol + bluecol*sexe + south + married, data=Wages)
print(summary(l2))
l3 <- lm( lwage ~ ed + exp + sexe:bluecol +south + married, data=Wages)
print(summary(l3))

l4 <- lm( lwage ~ ed + I(ed^2) + exp + I(exp^2) + sexe + bluecol + south + married, data=Wages)
print(summary(l4))

b <- coef(l4)
par(mfrow=c(1,2))
plot(Wages$ed, b[2]*Wages$ed + b[3]*Wages$ed^2)
plot(Wages$exp, b[4]*Wages$exp + b[5]*Wages$exp^2)
par(mfrow=c(1,1))

## one-way ANOVA
l <- lm( lower.frame ~ group, data=x )
print(l)
print(summary(l))
print(anova(l))

l1 <- lm( lower.frame ~ group -1, data=x )
print(l1)
print(summary(l1))
print(anova(l1))