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

model4 <- glm(complication ~ surgtype + longact + age + duration + I(tofratio - 0.7) +
              longact:I(tofratio - 0.7),
              data = surgery,
              family = binomial)

used.data <- na.omit(subset(surgery,select= c(complication, surgtype, age, longact, duration, tofratio)))

used.data$predicted <- model4$fitted

## choosing a type of quantiles which gives the same grouping as in SAS
used.data$decile <- cut(used.data$predicted,
                        breaks = quantile(used.data$predicted, seq(0,1,0.1), type = 6),
                        include.lowest = T, labels = 1:10)

cross <- xtabs(~ complication + decile, data = used.data)
print(addmargins(cross))

O <- cross[2,]
E <- tapply(used.data$predicted, used.data$decile, sum)


cbind(colSums(cross[1:2,]), O, E, (O-E)/sqrt(E))