As long as you do not show the whole line starting with -logit- it is hard
to tell what the problem is. Note that you could shorten the -numlist- for
the loop to 1/10 because your stepsize is 1. I am not sure what the -sort-
line is intended to accomplish. Obviously "anyprevcs" comes from the dataset
you open inside the loop?`
The line " replace dropouts =1 if anyprevcs==1 & (_N - _n) < 3575 " could
probably be done more elegantly with the -in - qualifier (in -3575/l).
HTH
Martin
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Martin Weiss
Sent: Friday, September 12, 2008 9:46 AM
To: [email protected]
Subject: RE: st: RE: Output summary stats before doing other iteration
True! Seems like a textbook case for -simulate- as I suggested yesterday
http://www.stata.com/statalist/archive/2008-09/msg00392.html.
In fact, I cannot see what is saved inside the loop. The -summarize- line
would throw up output for the screen, but if you want to save
characteristics of the predictions, -simulate- is the way to go...
HTH
Martin
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Eva Poen
Sent: Friday, September 12, 2008 9:31 AM
To: [email protected]
Subject: Re: st: RE: Output summary stats before doing other iteration
Larraine,
please don't use html for your postings to statalist (see the advice
in the faq).
It would be interesting to see the -if- statement in your logit
regression. Is it -if dropouts==1-? If I understand correctly, your
procedure is
- generate random variable for sorting
- run logistic regression on a (random) subsample of your data, which
excludes all observations that have been used in previous iterations
- generate predicted values for _all_ observations in the dataset.
First, whenever you work with random numbers, you should set the
random number seed in order to make your results reproducible:
set seed 123
for example.
Next, it seems perfectly sufficient to save the estimation result
during the loop. You can use
estimates store iteration`i'
within your loop, and then generate predicted values as and if you need
them:
estimates for iteration723 : predict onehat723
See -help estimates-. Do you really want to generate 1000 variables
with predicted values? If you tell us what you ultimately want to
achive, we might be able to suggest something more suitable. It looks
as if you are doing a simulation exercise of some sort; there might be
a more direct way.
Hope this helps,
Eva
2008/9/12 Larraine Becker <[email protected]>:
> Just a correction, the program below is wrong.it should be:
>
>
>
> forvalues i=1(1)10 {
>
> use "U:\CS\combined dataset_2006.dta", clear
>
> generate random`i' = uniform()
>
> sort anyprevcs random`i'
>
> generate dropouts = 0
>
> replace dropouts =1 if anyprevcs==1 & (_N - _n) < 3575
>
>
>
> logit ...(I've deleted the variables, as there are too many to put here!)
>
>
>
> replace anyprevcs=0 if dropouts==1
>
> predict onehat`i'
>
> summarize onehat`i'
>
> drop dropouts n
>
> }
>
>
>
>
>
> ________________________________
>
> From: [email protected]
> [mailto:[email protected]] On Behalf Of Larraine Becker
> Sent: Friday, 12 September 2008 4:35 PM
> To: [email protected]
> Subject: st: Output summary stats before doing other iteration
>
>
>
> Hi all,
>
>
>
> I'm doing 1000 iterations of a logistic regression. I have to output the
> predicted value each time before it carries on with the next iteration,
> otherwise I lose the
>
> first 999 predicted values! I'm sure there is a way to go about this, but
> how can I save the predicted value each time so I end up with a table with
> 1000 predicted values?
>
>
>
> My program is as follows:
>
>
>
> forvalues i=1(1)10 {
>
> use "U:\CS\combined dataset_2006.dta", clear
>
> generate random`i' = uniform()
>
> sort anyprevcs random`i'
>
> generate dropouts = 0
>
> replace dropouts =1 if anyprevcs==1 & (_N - _n) < 3575
>
>
>
> logit ...(I've deleted the variables, as there are too many to put here!)
>
>
>
> replace anyprevcs=0 if dropouts==1
>
> predict onehat`i'
>
> summarize onehat`i'
>
> gen n=_n
>
> egen predicted`i'=mean(onehat`i')*n
>
> drop dropouts n
>
> }
>
>
>
> Thanks,
>
> Larraine
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