source('../../Data/get_birthwt_data.R')
DF = get_birthwt_data()

## Lets look at how the variables interact:
##
pairs(DF)

## Fit a model:
##
m = lm(wt ~ ., data=DF)
print(summary(m))

## Plot the hat matrix diagonal against the fitted values:
##
inf = influence(m)
plot(m$fitted.values, inf$hat, type='p', pch=19, xlab='Fitted values', ylab='Hat diagonals')
ext = match(sort(inf$hat)[dim(DF)[1] - (0:3)], inf$hat)
text(m$fitted.values[ext], inf$hat[ext], labels=row.names(DF)[ext], pos=c(4, 4, 4, 4))
grid()

## Jackknife diagnostics:
##
if( F ){
    im = influence.measures(m)
    print(im)
}

print(DF[c(57, 31), ])