st: AW: R: RE: odd results after insample

["Martin Weiss" <martin.weiss1@gmx.de>]

<> 


Also note the help file for -simulate- which contains an example covering
the lognormal distribution...



HTH
Martin


-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Carlo Lazzaro
Gesendet: Samstag, 26. September 2009 16:20
An: statalist@hsphsun2.harvard.edu
Cc: 'Martin Weiss'
Betreff: st: R: RE: odd results after insample

Dear Martin,
thanks a lot for your kind reply.

The approach sketched in my previous message follows the one suggested by:
Briggs, A. and Nixon, R. and Dixon, S. and Thompson, S. (2005) Parametric
modelling of cost data: some simulation evidence. Health Economics 14(4):pp.
421-428.

So far, I have been quite successful with other Stata procedures for drawing
random samples from a given distribution (for instance, -simulate-),
including the approach you kindly advice me about.

Unfortunately, I cannot figure out what went wrong with this last do_file.

Thanks a lot again and enjoy your W_E.

Kind Regards,
Carlo
-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Martin Weiss
Inviato: sabato 26 settembre 2009 15.21
A: statalist@hsphsun2.harvard.edu
Oggetto: st: RE: odd results after insample


<>

Just out of curiosity: If you want 20 obs per sample, and 2,000 samples,
should that not lead to 40,000 observations overall?  


HTH
Martin

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Carlo Lazzaro
Sent: Samstag, 26. September 2009 15:00
To: statalist@hsphsun2.harvard.edu
Subject: st: odd results after insample

Dear Statalisters,
as an alternative to - simulate - , I have written the following do file
(for Stata 9.2/SE) to draw 2000 random samples, 20 observations each, from a
normal distribution: 

drop _all
set more off
set obs 2000
obs was 0, now 2000
g double ln_g_20=.
g double ln_sd_g_20=.
set seed 999
qui gen A=5.37 + 1.19*invnorm(uniform()) in 1/972
qui forvalues i = 1(1)2000 {
qui gen ln_20`i'=A
qui generate random`i' = uniform() 
qui sort random`i'
qui generate insample`i' = _n <= 20
qui sum ln_20`i' if insample`i' == 1
replace ln_g_20=r(mean)  in `i'
replace ln_sd_g_20=r(sd) in `i'
drop ln_20`i'
drop random`i' 
drop insample`i'
}
drop A

However, as a result I have obtained 1721 observations instead of the
expected 2000. 

sum ln_g_20 ln_sd_g_20

Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     ln_g_20 |      1271    5.314033    .3800687    3.79247   6.587941
  ln_sd_g_20 |      1271    1.101084    .2835007   .0260279   2.161299


Besides, results are even more puzzling when I increase the number of
samples (again 20 observations each), in that I get a different number of
observation for ln_g and ln_sd_g.

Comments are gratefully acknowledged.

Thanks a lot for your kindness and for your time.

Kind Regards,
Carlo


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