John Sorkin wrote:
Jumping into a thread can be like jumping into a den of lions but here goes . .
.
Sensitivity and specificity are not designed to determine the quality of a fit (i.e. if your model is good), but rather are characteristics of a test. A test that has high sensitivity will properly identify a large portion of people with a disease (or a characteristic) of interest. A test with high specificity will properly identify large proportion of people without a disease (or characteristic) of interest. Sensitivity and specificity inform the end user about the "quality" of a test. Other metrics have been designed to determine the quality of the fit, none that I know of are completely satisfactory. The pseudo R squared is one such measure.
For a given diagnostic test (or classification scheme), different cut-off points for identifying subject who have disease can be examined to see how they influence sensitivity and 1-specificity using ROC curves.
I await the flames that will surely come my way
John
John this has been much debated but I fail to see how backwards
probabilities are that helpful in judging the usefulness of a test. Why
not condition on what we know (the test result and other baseline
variables) and quit conditioning on what we are trying to find out
(disease status)? The data collected in most studies (other than
case-control) allow one to use logistic modeling with the correct time
order.
Furthermore, sensitivity and specificity are not constants but vary with
subjects' characteristics. So they are not even useful as simplifying
concepts.
Frank
John David Sorkin M.D., Ph.D.
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)
Frank E Harrell Jr <[EMAIL PROTECTED]> 10/13/2008 12:27 PM >>>
Maithili Shiva wrote:
Dear Mr Peter Dalgaard and Mr Dieter Menne,
I sincerely thank you for helping me out with my problem. The thing is taht I
already have calculated SENS = Gg / (Gg + Bg) = 89.97%
and SPEC = Bb / (Bb + Gb) = 74.38%.
Now I have values of SENS and SPEC, which are absolute in nature. My question
was how do I interpret these absolue values. How does these values help me to
find out wheher my model is good.
With regards
Ms Maithili Shiva
I can't understand why you are interested in probabilities that are in
backwards time order.
Frank
________________________________________________________________________
Subject: [R] Logistic regresion - Interpreting (SENS) and (SPEC)
To: r-help@r-project.org
Date: Friday, October 10, 2008, 5:54 AM
Hi
Hi I am working on credit scoring model using logistic
regression. I havd main sample of 42500 clentes and based on
their status as regards to defaulted / non - defaulted, I
have genereted the probability of default.
I have a hold out sample of 5000 clients. I have calculated
(1) No of correctly classified goods Gg, (2) No of correcly
classified Bads Bg and also (3) number of wrongly classified
bads (Gb) and (4) number of wrongly classified goods (Bg).
My prolem is how to interpret these results? What I have
arrived at are the absolute figures.
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
