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From |
"Carlo Lazzaro" <[email protected]> |

To |
<[email protected]> |

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

Date |
Sat, 26 Sep 2009 20:55:43 +0200 |

Dear Martin, many thanks for your unvaluable efforts. However, I am current interested in data reported in Table 3 and 4 of the article. I have not access to the related data sets and I am figuring out a way to mimick them and random sampling from the obtained (far fetched) distributions. Thanks a lot again, especially for downloading the paper and devoting your time to think it over? Out of curiosity: is the economic evaluation of health care programmes one of your research fields? Kind Regards, Carlo -----Messaggio originale----- Da: [email protected] [mailto:[email protected]] Per conto di Martin Weiss Inviato: sabato 26 settembre 2009 19.51 A: [email protected] Oggetto: st: AW: R: RE: odd results after insample <> So if you wanted the entire figure 1 under Stata -version- 9.2, you would probably want to install Bobby`s -findit gendist- and then: ************* clear* vers 9.2 //lognormal part capt prog drop myprog prog def myprog, rclass vers 9.2 syntax newvarname(numeric max=1), [obs(integer 100) cov(real 2)] set obs `obs' loc sd = sqrt(log(`cov'^2+1)) loc mean = log(1000)-.5*`sd'^2 gen `varlist'=exp(invnormal(uniform())*`sd'+`mean') qui su `varlist' ret sca mean=r(mean) ret sca cv=r(sd)/r(mean) end loc gra loc j 1 //for sample size 2000 foreach cv in 0.25 0.5 1 1.5 2{ myprog lognorm`j', obs(2000) cov(`cv') loc gra `gra' (kdensity lognorm`j' if lognorm`j'<3000) loc ++j } //see the mean and coeff of variation tabstat _all, stat(mean cv sd) tw `gra', legend(off) nodraw /* */ name(lognormal, replace) //gamma part capt prog drop mynewprog prog def mynewprog, rclass vers 9.2 syntax newvarname(numeric max=1) [,obs(integer 100) cov(real 2)] set obs `obs' loc shape = (`cov')^(-2) loc scale = 1000/`shape' gengamma `varlist', alpha(`shape') beta(`scale') qui su `varlist' ret sca mean=r(mean) ret sca cv=r(sd)/r(mean) end loc gra loc j 1 //for sample size 2000 foreach cv in 0.25 0.5 1 1.5 2{ mynewprog gamma`j', obs(2000) cov(`cv') loc gra `gra' (kdensity gamma`j' if gamma`j'<3000) loc ++j } //see the mean and coeff of variation tabstat _all, stat(mean cv sd) tw `gra', legend(off) /* nodraw */ name(gamma, replace) //combine 'em gr combine lognormal gamma, /* */ cols(1) ************* HTH Martin -----Ursprüngliche Nachricht----- Von: [email protected] [mailto:[email protected]] Im Auftrag von Carlo Lazzaro Gesendet: Samstag, 26. September 2009 16:20 An: [email protected] 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: [email protected] [mailto:[email protected]] Per conto di Martin Weiss Inviato: sabato 26 settembre 2009 15.21 A: [email protected] 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: [email protected] [mailto:[email protected]] On Behalf Of Carlo Lazzaro Sent: Samstag, 26. September 2009 15:00 To: [email protected] 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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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**st: AW: R: AW: R: RE: odd results after insample***From:*"Martin Weiss" <[email protected]>

**References**:**st: AW: R: RE: odd results after insample***From:*"Martin Weiss" <[email protected]>

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