[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
Gary Longton <[email protected]> |

To |
[email protected] |

Subject |
st: Re: saving simulation results (was: Any capability) |

Date |
Fri, 10 Sep 2004 10:46:20 -0700 |

victor michael zammit wrote:

Victor included code for a program which saved the mean and sd from 1000 simulated samples, sample size given as an argument, drawn from ~N(mu=50,sigma=10).Dear Statalist, given a do-file ,such as the one that follows , is there any capability to save the valid variables before the program gets to the next < set obs `1' > ,and thus be able to find the relevant variables again after the program is through ?

Though it is not clear what you mean by "valid variables", I'm guessing that you want to save a file with the *results* for each set of simulations rather than variables generated for the actual samples.

See the -postfile- commands ([P]postfile) for an easy way to write results from each simulation to a file. This should speed things up substantially in addition to making for a tidier solution; you will not need to save and merge results from each simulation.

Though I prefer to use the -postfile- commands, see [R]simulate for an even easier and higher-level solution.

Here's a re-write which uses the -postfile- command to save results in the specified result file, and includes arguments for the sample size (as before), number of simulations, Normal distn parameters, and the result file name.

******************

cap pro drop meansd

pro def meansd

version 8.2

args nobs nsim mu sigma resfile

clear

set obs `nobs'

tempname pfile

postfile `pfile' mean sd simno using `resfile', replace

qui gen x = .

forvalues i = 1/`nsim' {

qui replace x = `mu' + `sigma'*invnorm(uniform())

qui sum x

post `pfile' (r(mean)) (r(sd)) (`i')

}

postclose `pfile'

end

**********************

meansd 100 1000 50 10 result1

meansd 200 1000 50 10 result2

meansd 300 1000 50 10 result3

meansd 400 1000 50 10 result4

use result1, clear

l if mean>49.9 & mean<50.1 & sd>9.9 & sd<10.1

etc.

-------------------------------------------------------

Alternatively, a solution using -simul-:

***********************

cap pro drop meansd

pro def meansd, rclass

version 8.2

args nobs mu sigma

drop _all

set obs `nobs'

gen x = `mu' + `sigma'*invnorm(uniform())

sum x

return scalar mean = r(mean)

return scalar sd = r(sd)

end

**********************

simul "meansd 100 50 10" mean=r(mean) sd=r(sd), reps(1000) saving(result1) replace

simul "meansd 200 50 10" mean=r(mean) sd=r(sd), reps(1000) saving(result2) replace

simul "meansd 300 50 10" mean=r(mean) sd=r(sd), reps(1000) saving(result3) replace

simul "meansd 400 50 10" mean=r(mean) sd=r(sd), reps(1000) saving(result4) replace

forvalues i = 1/4 {

use result`i', clear

l if mean>49.9 & mean<50.1 & sd>9.9 & sd<10.1

}

-----------

- Gary

*

* For searches and help try:

* http://www.stata.com/support/faqs/res/findit.html

* http://www.stata.com/support/statalist/faq

* http://www.ats.ucla.edu/stat/stata/

**References**:**st: Re: Scheme error message***From:*Friedrich Huebler <[email protected]>

**st: Re: Any capability***From:*"victor michael zammit" <[email protected]>

- Prev by Date:
**st: RE: Squared correlation** - Next by Date:
**st: Correlation coefficients and the CORTESTi program** - Previous by thread:
**st: Re: Any capability** - Next by thread:
**st: Re:Is there any way ?** - Index(es):

© Copyright 1996–2024 StataCorp LLC | Terms of use | Privacy | Contact us | What's new | Site index |