Re: mixture distribution

On Thu, 22 May 2008 13:40:55 -0700 (PDT), W <wzhang999@xxxxxxxxx>
wrote:

Hello:

I have a group of diagnosis test data, 40 of which were from positive
samples and 200 of which were from negative samples. I want to
calculate a cutoff value to separate the pos and negs so that I can
classify the future data points to either positive or negative group.
Pretending I didn't know the label of samples, I used a mixed normal
model and optimized a cutoff value but the seperation wasn't very
good. When I went back to validate using the sample labels, I found
too many (30%) negative values were classified as positive values
(sensitivity was high but specificity was low). The code I used was
writen in R. I first did a guess what the mean and sd could be for the
positive and negtive values could be, and then do optim() for
optimization. Is there any better way to do this?

Thanks much!

You have to make a verbal argument to justify
whatever you get. I suggest starting with the
verbal argument.

Sort them into order, and draw the cut-off
where you like it best. Equal fraction of errors
in either direction?

Are the errors less desirable for specificity?
- weight them differently.

[snip]

--
Rich Ulrich
http://www.pitt.edu/~wpilib/index.html
.