• #### Q would acc, specificity, and sensitivity, ect greater than 1

From Cosine@21:1/5 to All on Mon Aug 5 03:12:54 2019
Hi:

WOuld it be possible for any of the following to have a value greater than 1?

accuracy, specificity, sensitivity, precision, and recall

Say, if a set of samples was taken, the Var_avg +- std would be > 1?

where Var stands for accuracy, specificity, sensitivity, precision, or recall

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• From Rich Ulrich@21:1/5 to All on Mon Aug 5 13:30:24 2019
On Mon, 5 Aug 2019 03:12:54 -0700 (PDT), Cosine <asecant@gmail.com>
wrote:

Hi:

WOuld it be possible for any of the following to have a value greater than 1?

accuracy, specificity, sensitivity, precision, and recall

Of course not.

Say, if a set of samples was taken, the Var_avg +- std would be > 1?

where Var stands for accuracy, specificity, sensitivity, precision, or recall

Sure, you can compute a CI that is exceeds the bounds of
(0,1) if you use simplistic formulas. But for any proportion
that is not 0.50, the more accurate statement of "error" is
never symmetric, so you would not write "+- std" if you
intended to be precise.

And - Even for 50%, the symmetrical CI will be too wide
if you use the simplisitic formulas. Consider the 99% or
99.9% (or even more extreme) CI for any small sample.

--
Rich Ulrich

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• From Cosine@21:1/5 to All on Mon Aug 5 21:08:46 2019
Rich Ulrich於 2019年8月6日星期二 UTC+8上午1時30分30秒寫道：
On Mon, 5 Aug 2019 03:12:54 -0700 (PDT), Cosine
wrote:

Hi:

WOuld it be possible for any of the following to have a value greater than 1?

accuracy, specificity, sensitivity, precision, and recall

Of course not.

Say, if a set of samples was taken, the Var_avg +- std would be > 1?

where Var stands for accuracy, specificity, sensitivity, precision, or recall

Sure, you can compute a CI that is exceeds the bounds of
(0,1) if you use simplistic formulas. But for any proportion
that is not 0.50, the more accurate statement of "error" is
never symmetric, so you would not write "+- std" if you
intended to be precise.

And - Even for 50%, the symmetrical CI will be too wide
if you use the simplisitic formulas. Consider the 99% or
99.9% (or even more extreme) CI for any small sample.

--
Rich Ulrich

Thank you for the explanation.

But how do we interpret the part exceeding [0, 1]?

Say, how do we interpret those values of accuracy being negative or greater than 1? Does it mean that those exceeding [0, 1] are meaningless?

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• From Rich Ulrich@21:1/5 to All on Wed Aug 7 13:32:59 2019
On Mon, 5 Aug 2019 21:08:46 -0700 (PDT), Cosine <asecant@gmail.com>
wrote:

Rich Ulrich? 2019?8?6???? UTC+8??1?30?30????
On Mon, 5 Aug 2019 03:12:54 -0700 (PDT), Cosine
wrote:

Hi:

WOuld it be possible for any of the following to have a value greater than 1?

accuracy, specificity, sensitivity, precision, and recall

Of course not.

Say, if a set of samples was taken, the Var_avg +- std would be > 1?

where Var stands for accuracy, specificity, sensitivity, precision, or recall

Sure, you can compute a CI that is exceeds the bounds of
(0,1) if you use simplistic formulas. But for any proportion
that is not 0.50, the more accurate statement of "error" is
never symmetric, so you would not write "+- std" if you
intended to be precise.

And - Even for 50%, the symmetrical CI will be too wide
if you use the simplisitic formulas. Consider the 99% or
99.9% (or even more extreme) CI for any small sample.

--
Rich Ulrich

Thank you for the explanation.

But how do we interpret the part exceeding [0, 1]?

Say, how do we interpret those values of accuracy being negative or greater than 1? Does it mean that those exceeding [0, 1] are meaningless?

How do YOU interpret a claim (say) that there will be
"minus three new cases"? "Not sensible" comes to mind.

I suppose ithat the inapt Confidence interval gives some
warning that over-simplified statistics have been applied.
And that the other side of the interval probably should
be fatter.

--
Rich Ulrich

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• From Bruce Weaver@21:1/5 to Bruce Weaver on Mon Aug 12 11:57:43 2019
On Monday, August 12, 2019 at 2:53:38 PM UTC-4, Bruce Weaver wrote:

Consider using one of the good approximate methods for computing your CIs--e.g., the Wilson score method or the Agresti-Coull method. Here's an online calculator that implements both of those:

http://epitools.ausvet.com.au/content.php?page=CIProportion

If you want SPSS code to compute these CIs, see syntax files ciprop.SPS and ciprop2.SPS on my SPSS page:

You should be able to lift the code for the method you want to use fairly easily. E.g., if you want to use the Wilson score method, here is the basic code:

* Wilson score method (Method 3 in Newcombe, 1998) .
* Code adapted from Robert Newcombe's code posted here:
http://archive.uwcm.ac.uk/uwcm/ms/Robert2.html .

COMPUTE p = x/n.
COMPUTE q = 1-p.
COMPUTE z = probit(1-alpha/2).

COMPUTE #x1 = 2*n*p+z**2 .
COMPUTE #x2 = z*(z**2+4*n*p*(1-p))**0.5 .
COMPUTE #x3 = 2*(n+z**2) .
COMPUTE lower4 = (#x1 - #x2) / #x3 .
COMPUTE upper4 = (#x1 + #x2) / #x3 .

I used lower4 and upper4 because this was method 4 in the original syntax file. You can change it to lower and upper if you are using just one method. HTH.

Oops. For some reason, I thought I had seen that the OP uses SPSS. That's why I posted SPSS code. But now I see there was no mention of SPSS. Even so, if you bear in mind that ** indicates exponentiation in SPSS code, it should be relatively easy to
translate to another stats package. (Also note that # as the first character of a variable name indicates a "scratch" variable, or a temporary variable that disappears after all computations have been executed.)

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• From Bruce Weaver@21:1/5 to Cosine on Mon Aug 12 11:53:36 2019
On Tuesday, August 6, 2019 at 12:08:48 AM UTC-4, Cosine wrote:
Rich Ulrich於 2019年8月6日星期二 UTC+8上午1時30分30秒寫道：
On Mon, 5 Aug 2019 03:12:54 -0700 (PDT), Cosine
wrote:

Hi:

WOuld it be possible for any of the following to have a value greater than 1?

accuracy, specificity, sensitivity, precision, and recall

Of course not.

Say, if a set of samples was taken, the Var_avg +- std would be > 1?

where Var stands for accuracy, specificity, sensitivity, precision, or recall

Sure, you can compute a CI that is exceeds the bounds of
(0,1) if you use simplistic formulas. But for any proportion
that is not 0.50, the more accurate statement of "error" is
never symmetric, so you would not write "+- std" if you
intended to be precise.

And - Even for 50%, the symmetrical CI will be too wide
if you use the simplisitic formulas. Consider the 99% or
99.9% (or even more extreme) CI for any small sample.

--
Rich Ulrich

Thank you for the explanation.

But how do we interpret the part exceeding [0, 1]?

Say, how do we interpret those values of accuracy being negative or greater than 1? Does it mean that those exceeding [0, 1] are meaningless?

Consider using one of the good approximate methods for computing your CIs--e.g., the Wilson score method or the Agresti-Coull method. Here's an online calculator that implements both of those:

http://epitools.ausvet.com.au/content.php?page=CIProportion

If you want SPSS code to compute these CIs, see syntax files ciprop.SPS and ciprop2.SPS on my SPSS page:

You should be able to lift the code for the method you want to use fairly easily. E.g., if you want to use the Wilson score method, here is the basic code:

* Wilson score method (Method 3 in Newcombe, 1998) .
* Code adapted from Robert Newcombe's code posted here:
http://archive.uwcm.ac.uk/uwcm/ms/Robert2.html .

COMPUTE p = x/n.
COMPUTE q = 1-p.
COMPUTE z = probit(1-alpha/2).

COMPUTE #x1 = 2*n*p+z**2 .
COMPUTE #x2 = z*(z**2+4*n*p*(1-p))**0.5 .
COMPUTE #x3 = 2*(n+z**2) .
COMPUTE lower4 = (#x1 - #x2) / #x3 .
COMPUTE upper4 = (#x1 + #x2) / #x3 .

I used lower4 and upper4 because this was method 4 in the original syntax file. You can change it to lower and upper if you are using just one method. HTH.

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