[R] Fw: Logistic regresion - Interpreting (SENS) and (SPEC)

[Pedro.Rodriguez at sungard.com]

Hi Maithili,

There are two good papers that illustrate how to compare classifiers
using Sensitivity and Specificity and their extensions (e.g., likelihood
ratios, young index, KL distance, etc).

See: 
1) Biggerstaff, Brad, 2000, "Comparing diagnostic tests: a simple
graphic using likelihood ratios," Statistics in Medicine, 19:649-663.

2) Lee, Wen-Chung, 1999, "Selecting diagnostic tests for ruling out or
ruling in disease: the use of the Kllback-Leibler distance,"
International Epidemiological Association, 28:521-525.

Please let me know if have problems finding the aforementioned papers.

Kind Regards,

Pedro


-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On Behalf Of Maithili Shiva
Sent: Monday, October 13, 2008 3:28 AM
To: r-help at r-project.org
Cc: dieter.menne at menne-biomed.de; p.dalgaard at biostat.ku.dk
Subject: [R] Fw: Logistic regresion - Interpreting (SENS) and (SPEC)


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

________________________________________________________________________






> Subject: [R] Logistic regresion - Interpreting (SENS) and (SPEC)
> To: r-help at 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.
> 
> ______________________________________________
> R-help at r-project.org mailing list
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> and provide commented, minimal, self-contained,
> reproducible code.

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