Missing data problem and ROC curves

Hello all,

Trying to get this piece of code to work on my data set. It is from
http://www.itc.nl/personal/rossiter.

logit.roc <- function(model, steps=100)
		{
		field.name <- attr(attr(terms(formula(model)), "factors"),
"dimnames")[[1]][1]
		eval(parse(text=paste("tmp <- ", ifelse(class(model$data) == "data.frame",
"model$data$", ""), field.name, sep="")))
		r <- data.frame(pts = seq(0, 1-(1/steps), by=1/steps), sens = 0, spec=0);
for (i in 0:steps)
		{
		      thresh <- i/steps;
		      r$sens[i] <- sum((fitted(model) >= thresh) & tmp)/sum(tmp);
		      r$spec[i] <- sum((fitted(model) < thresh) & !tmp)/sum(!tmp)
		}
		return(r)}

where model is the output of a glm.

The problem is the "sum((fitted(model) >= thresh) & tmp)" bit. The lengths
of fitted(model) and tmp are not equal because some of the cases were
deleted from the model due to missing data! fitted(model) is a set of named
numbers while tmp is a set of integers.

My question is:
- How do I determine which cases were deleted from the model and then delete
the associated cases from tmp?