[R] Missing data problem and ROC curves
tfj24
t.jones at smd11.qmul.ac.uk
Mon Jul 1 19:57:04 CEST 2013
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?
I hope this makes sense and would really appreciate any help that people may
have.
Thanks,
Tim
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