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

surgery$age60 <- (surgery$age-60)/10
surgery$dur60 <- surgery$duration/60-1
surgery$tof07 <- (surgery$tofratio-0.7)*10

model <- glm(complication ~ surgtype + age60 + dur60 +
             longact + longact:tof07,
             family = binomial,
             data = surgery)

## the rows in surgery used in the fit
used.data <- surgery[rownames(model$model),]

par(mfrow = c(2,2))

## cooks distance
plot(cooks.distance(model) ~ age,
     data = used.data,
     xlab = "Age",
     ylab = "Cook's distance",
     pch = ifelse(used.data$complication == 1, 4, 21)
     ## cross = complication, o = no complication.
     )

## influential measures for all coefficients.
## The R-function "dfbetas" gives similar quantities
dfb <- function(){
  se <- sqrt(diag(summary(model)$cov.unscaled)) 
  refit <- function(i){ ## fit without observation i
    model.i <- update(model, data = used.data[-i,])
    (model$coef - model.i$coef)/se
  }
  t(sapply(1:nrow(used.data), refit))
}

dfbetas <- dfb()

plot(subset(dfbetas, select = age60) ~ age,
     data = used.data,
     xlab = "Age",
     ylab = "Influence, age",
     pch = ifelse(used.data$complication == 1, 4, 21)
     )

plot(subset(dfbetas, select = dur60) ~ duration,
     data = used.data,
     xlab = "Duration of anesthesia",
     ylab = "Influence, duration",
     pch = ifelse(used.data$complication == 1, 4, 21)
     )

plot(subset(dfbetas, select = which(names(model$coef) == "longact1:tof07")) ~ tofratio,
     data = used.data,
     xlab = "TOF-ratio",
     ylab = "Influence, TOF-ratio and long-act interaction",
     pch = ifelse(used.data$complication == 1, 4, 21)
     )