Great code. Thanks. Some questions:
1) Is there any document explaning the output - what each result means and how is it calculated?
2) We found that a negative value in the data causing an error. Is that true? Will adding a constant to bring all data above zero will affect the results?
I have encountered a problem with your implementation and seeking your help. The PDFs obtained using translated versions of the signal (image histogram, in this case) is not the same.
data = [23 23 23 22 22 22 21 22 23];
data = [53 53 53 52 52 52 51 52 53];
MIN = 0
MAX = 255
n = 256;
This gives a good uni-modal estimate, whereas the second one is incomprehensible.
Please take a look at the density plots in each case.
This might be a problem with the bandwidth estimation but I don't know how to solve it.
Any help is appreciated.
Noam, the message you get is from a previous version of roc.m routine and not from partest.m. When you have a test, using roc curve analysis you can choose a cut-off point (the point above/below which the test is positive) to obtain the max sensitivity or the max specificity or the max cost effective or the max efficiency or the max positive predictive value...and this choosing depends on your specific problem. Actually, I erased partest invoking from roc to simplify the code.
Anyway, Partest asks whether you want to input the true prevalence of a disease or not. Partest uses the Bayes'es Theorem and the true prevalence is needed to correctly apply it
Thanks for the code.
I looked at the source and didn't find an explanation for the "Choose cut-off to compute performance" message. What does it mean? and what values are relevant?
And one more question: I run partest within a loop and now I need to press enter every time the cut-off message appears. How can set it to run for the same value everytime it runs?