source("http://192.38.117.59/~linearpredictors/datafiles/readFever.R")

## sorting the data according to alcohol consumption
fever <- fever[order(fever$alco),]

## Fitting linear model with alcohol as explanatory variable
model <- glm(death ~ alco, 
             family = binomial,
             data = fever)

## Observed death probabilities smoothed on evenly distributed values 
## of alcohol consumption

smoothed.deaths <- predict(
                   loess(unclass(death)-1 ~ alco, data = fever,
                          degree = 1, span = 0.8),
                    data.frame(alco = seq(0, 15, .1))
                    )

## Smoothed observed deaths (probabilities) on log-odds scale
plot(seq(0,15,.1), log(smoothed.deaths/(1-smoothed.deaths)),
     type = "l",
     xlab = "Drinks per week",
     ylab = "Fetal death, log(odds) scale",
     ylim = c(-5,-3)
     )
lines((model$model)$alco, predict(model, type="link"), lty="22")

## Marginal histogram
hist <- hist(fever$alco, breaks = seq(-0.25,15.25,0.5), plot = FALSE) 
p <- hist$mids[hist$counts != 0]
counts <- hist$counts[hist$counts != 0]
segments(p, -5, p, -5 + counts/max(counts)*.2)