Re: st: error message about bootstrp

[Wu Zhang <wuzhang50@yahoo.com>]

Hi Nick,

  That's very smart way to do it; by the way, can you change the program for arbitrary many variables, not just for 2?

Thanks a lot!

Wu 



________________________________
From: Nick Cox <njcoxstata@gmail.com>
To: statalist@hsphsun2.harvard.edu 
Sent: Monday, March 4, 2013 2:02 PM
Subject: Re: st: error message about bootstrp

Yes; you can use e(b) in a r-class program.

The best way to check is by doing, i.e. by writing a dopey little
program, such as

program myprog, rclass
       version 8
       syntax varlist(numeric min=2 max=2)
       regress `varlist'
       tempname b
       mat `b' = e(b)
       return scalar b1 = `b'[1,1]
       return scalar b2 = `b'[1,2]
end


Nick

On Mon, Mar 4, 2013 at 7:54 PM, Wu Zhang <wuzhang50@yahoo.com> wrote:
> Hi Nick,
>
>   It works fine that way.I am just wondering whether we can use e(b) in the rclass program?
>
> Thanks,
>
> Wu
>
>
>
>
> ----- Original Message -----
> From: Nick Cox <njcoxstata@gmail.com>
> To: statalist@hsphsun2.harvard.edu
> Cc:
> Sent: Monday, March 4, 2013 7:27 AM
> Subject: Re: st: error message about bootstrp
>
> You should run your program -apese- once by itself on the data to see
> what it leaves behind.
>
> Nick
>
> On Mon, Mar 4, 2013 at 7:54 AM, Wu Zhang <wuzhang50@yahoo.com> wrote:
>
>> After I adopt your suggestion and modify the code a little bit, I get this error message :
>>
>> insufficient observations to compute bootstrap standard errors
>> no results will be saved
>> r(2000);
>>
>> The Boostrap code is as follows:
>>
>>
>>
>> cap program drop apese
>> program apese, rclass
>> version 12.1
>> capture drop   xalphat ehat sdhat meanvaluen meanvalue21
>> reg odds  x1 x2 x3
>> mat paraest=e(b)
>> return scalar alphaj =_b[x2]
>> predict ehat, residual
>> mean x1 x2 x3 const
>> mat xmean=e(b)
>> mat meanvalue=xmean*paraest'
>> svmat meanvalue, names(meanvalue2)
>> sum meanvalue21
>> gen meanvaluen=r(mean)
>> gen sdhat=alphaj*exp(meanvaluen+ehat)/(1+exp(meanvaluen+ehat))^2
>>
>> sum sdhat
>> return scalar apej=r(mean)
>>
>> end
>>
>>
>>
>> The running code is:  bootstrap apej=r(apej): apese
>>
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