Gad Abraham wrote:
Frank E Harrell Jr wrote:
Gad Abraham wrote:
This approach leaves much to be desired. I hope that its
practitioners start gauging it by the mean squared error of
predicted probabilities.
Is the logic here is that low MSE of predicted probabilities equals a
better calibra
Frank E Harrell Jr wrote:
Gad Abraham wrote:
This approach leaves much to be desired. I hope that its
practitioners start gauging it by the mean squared error of predicted
probabilities.
Is the logic here is that low MSE of predicted probabilities equals a
better calibrated model? What abou
Gad Abraham wrote:
This approach leaves much to be desired. I hope that its
practitioners start gauging it by the mean squared error of predicted
probabilities.
Is the logic here is that low MSE of predicted probabilities equals a
better calibrated model? What about discrimination? Perfect c
This approach leaves much to be desired. I hope that its practitioners
start gauging it by the mean squared error of predicted probabilities.
Is the logic here is that low MSE of predicted probabilities equals a
better calibrated model? What about discrimination? Perfect calibration
implies p
Gabor Grothendieck wrote:
On Mon, Oct 13, 2008 at 11:47 PM, Frank E Harrell Jr
<[EMAIL PROTECTED]> wrote:
Gabor Grothendieck wrote:
On Mon, Oct 13, 2008 at 11:21 PM, Frank E Harrell Jr
<[EMAIL PROTECTED]> wrote:
[EMAIL PROTECTED] wrote:
I recall a concept of Snout: sensitivity that is high e
On Mon, Oct 13, 2008 at 11:47 PM, Frank E Harrell Jr
<[EMAIL PROTECTED]> wrote:
> Gabor Grothendieck wrote:
>>
>> On Mon, Oct 13, 2008 at 11:21 PM, Frank E Harrell Jr
>> <[EMAIL PROTECTED]> wrote:
>>>
>>> [EMAIL PROTECTED] wrote:
I recall a concept of Snout: sensitivity that is high enou
Gabor Grothendieck wrote:
On Mon, Oct 13, 2008 at 11:21 PM, Frank E Harrell Jr
<[EMAIL PROTECTED]> wrote:
[EMAIL PROTECTED] wrote:
I recall a concept of Snout: sensitivity that is high enough to
essentially rule out the presence of disease. And Spin: specificity that
is high enough to essent
/2008 4:41 PM >>>
- Original Message -
From: "Frank E Harrell Jr" <[EMAIL PROTECTED]>
To: "John Sorkin" <[EMAIL PROTECTED]>
Cc: ; <[EMAIL PROTECTED]>;
<[EMAIL PROTECTED]>
Sent: Monday, October 13, 2008 2:09 PM
Subject: Re: [R] Fw: Lo
On Mon, Oct 13, 2008 at 11:21 PM, Frank E Harrell Jr
<[EMAIL PROTECTED]> wrote:
> [EMAIL PROTECTED] wrote:
>>
>> I recall a concept of Snout: sensitivity that is high enough to
>> essentially rule out the presence of disease. And Spin: specificity that
>> is high enough to essentially rule in th
r, Ph.D." <[EMAIL PROTECTED]> 10/13/2008 4:41 PM >>>
- Original Message -
From: "Frank E Harrell Jr" <[EMAIL PROTECTED]>
To: "John Sorkin" <[EMAIL PROTECTED]>
Cc: ; <[EMAIL PROTECTED]>;
<[EMAIL PROTECTED]>
Sent: Monday, O
/diagnostictests.html
--Chris Ryan
Original message
Date: Mon, 13 Oct 2008 18:14:39 -0400
From: "John Sorkin" <[EMAIL PROTECTED]>
Subject: Re: [R] Fw: Logistic regresion - Interpreting (SENS) and (SPEC)
To: "Ph.D. Robert W. Baer" <[EMAIL PROTECTED]>, "Frank
Robert W. Baer, Ph.D. wrote:
- Original Message - From: "Frank E Harrell Jr"
<[EMAIL PROTECTED]>
To: "John Sorkin" <[EMAIL PROTECTED]>
Cc: ; <[EMAIL PROTECTED]>;
<[EMAIL PROTECTED]>
Sent: Monday, October 13, 2008 2:09 PM
Subject: Re: [R]
AIL
PROTECTED]
Sent: Monday, October 13, 2008 4:14 PM
To: Ph.D. Robert W. Baer; Frank E Harrell Jr
Cc: r-help@r-project.org; [EMAIL PROTECTED]; [EMAIL PROTECTED]
Subject: Re: [R] Fw: Logistic regresion - Interpreting (SENS) and (SPEC)
Of course Prof Baer is correct the positive predictive value (PP
the higher the specificity, the greater the PPV?
http://www.musc.edu/dc/icrebm/diagnostictests.html
--Chris Ryan
Original message
>Date: Mon, 13 Oct 2008 18:14:39 -0400
>From: "John Sorkin" <[EMAIL PROTECTED]>
>Subject: Re: [R] Fw: Logistic regresion - Interp
Indeed, however as I stated in my prior Email, the cases of a 0 or 1
prevalence are degenerative and are of little practical importance. And
as noted in my EMail message, I was talking about values of PPV and NPV
as a function of sensitivity and specificity when the prevalence is
fixed.
John
John
John Sorkin wrote:
Of course Prof Baer is correct the positive predictive value (PPV)
and the negative predictive values (NPV) serve the function of
providing conditional post-test probabilities PPV: Post-test
probability of disease given a positive test NPV: Post-test
probability of no disease g
gt;>
- Original Message -
From: "Frank E Harrell Jr" <[EMAIL PROTECTED]>
To: "John Sorkin" <[EMAIL PROTECTED]>
Cc: ; <[EMAIL PROTECTED]>;
<[EMAIL PROTECTED]>
Sent: Monday, October 13, 2008 2:09 PM
Subject: Re: [R] Fw: Logistic regresion - Int
- Original Message -
From: "Frank E Harrell Jr" <[EMAIL PROTECTED]>
To: "John Sorkin" <[EMAIL PROTECTED]>
Cc: ; <[EMAIL PROTECTED]>;
<[EMAIL PROTECTED]>
Sent: Monday, October 13, 2008 2:09 PM
Subject: Re: [R] Fw: Logistic regresion - I
John Sorkin wrote:
Frank,
Perhaps I was not clear in my previous Email message. Sensitivity and specificity do tell us about the quality of a test in that given two tests the one with higher sensitivity will be better at identifying subjects who have a disease in a pool who have a disease, and th
Frank,
Perhaps I was not clear in my previous Email message. Sensitivity and
specificity do tell us about the quality of a test in that given two tests the
one with higher sensitivity will be better at identifying subjects who have a
disease in a pool who have a disease, and the more sensitive t
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 proper
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
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 n
sungard.com> writes:
> 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
> graphi
forementioned papers.
Kind Regards,
Pedro
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of Maithili Shiva
Sent: Monday, October 13, 2008 3:28 AM
To: r-help@r-project.org
Cc: [EMAIL PROTECTED]; [EMAIL PROTECTED]
Subject: [R] Fw: Logistic regresion - Int
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
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