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RE: st: re: Cumulative Accuracy Profile

From   Drosophilia Melanogaster <>
Subject   RE: st: re: Cumulative Accuracy Profile
Date   Fri, 4 Feb 2011 09:52:38 +0800 (SGT)

Hi Nick, thanks for your concern. I was made aware of that error many years ago after I set up this false account. I never really bothered to correct it seeing that it would serve little purpose to me. And my first choice of name was Desmodus rotundus but the common fruit fly's name had a catchier sound to it. I was using the melanogaster as a surname so I thought to capitalize it. Haha. Anyways, I am only using this email now because after I tried using my company email to post in this thread, my mailbox was suddenly flooded with spam. ^__^

Anyhoo, thanks for your advice regarding my programming problem. I really have no idea how to program in Stata but ill give it a try nonetheless.

And thanks Jose Ricardo for taking my side. It's no issue, really. :)

Warmest regards,



The poster has a small problem of not being able to spell their own name correctly. I offered advice and explanation.  


Jose Ricardo Nogueira

No one is claiming to a common fruit fly, so what's your problem?

On Thu, Feb 3, 2011 at 3:18 AM, Nick Cox <> wrote:
> The name for the common fruit fly is Drosophila [not ...philia]
> melanogaster. Genus names are capitalised, specific names are not.
> In terms of the question, if you are minimising RMSE you are also
> minimising its square. In other words, this problem is nonlinear
> linear squares and there is absolutely no need to program it yourself.
> Head straight for the help and the manual entry for -nl-.
> Nick
> On Thu, Feb 3, 2011 at 5:52 AM, Drosophilia Melanogaster
> <> wrote:
>> How does one program an algorithm that estimates the concavity of a Cumulative Accuracy Profile (CAP) curve? I wish to fit a model for y using the function:
>> y = [1- exp(-kx)]/[1-exp(-k)]
>> where k is my parameter of interest and is obtained by minimizing a root mean square  error (RMSE) of the form:
>> e = sqrt{(1/N)*summation i to n of [y_i - [1- exp(-kx_i)]/[1-exp(-k)]]^2}.
>> The RMSE is minimized by applying a Newton-Raphson procedure using the following iteration (i=1,2... 20):
>> k_i+1 = k_i - [(de/dk)/(d2e/dk2)]     (ratio of derivatives)

>>Any help will be greatly appreciated as I am really at a loss when it >>comes to programming. >__<

>>Thanks in advance!

>>Warm regards,


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