Should I use bsample or rhsbsample

[Luis Mijares Castaneda]

Hello,

I'm trying to manually replicate the svyset command because the user-generated command arhomme is not compatible with svyset and svy. The code I wrote is shown below, and I was wondering whether it is more appropriate to sample using bsample or rhsample. I know that rhsample takes half samples of N/2, but I’m not sure if this command is better suited for replicating svyset manually.

Any guidance would be appreciated.

****bootstrap survey design SE***** 
*Step 1, save observed coefficients**** 
preserve
quietly xi:  arhomme log_avrg_cost i.inc_d i.endentulism i.race i.age_cat i.male i.education i.veteran i.mothered i.wealth i.smoke_now chronicdisease[pw=new_weight], select(r11dentst = dentalinsurance_w1 endentulism inc_d race age_cat male education veteran mothered wealth smoke_now chronicdisease)  quantiles(0.10, 0.25, 0.50, 0.75, 0.90)  taupoints(29) rhopoints(35)  meshsize(0.5) frank nostderrors  centergrid(-0.20)

matrix beta = e(b) 
global N "`e(N)'"
global Ns "`e(sN)'" 
restore 

** step 2 generate program for bootstrap****
capture program drop arhomme_bootstrap 
program arhomme_bootstrap, eclass  
preserve
bsample, strata(raestrat) cluster(raehsamp) 

quietly xi:  arhomme log_avrg_cost i.inc_d i.endentulism i.race i.age_cat i.male i.education i.veteran i.mothered i.wealth i.smoke_now chronicdisease[pw=new_weight], select(r11dentst = dentalinsurance_w1 endentulism inc_d race age_cat male education veteran mothered wealth smoke_now chronicdisease) quantiles(0.10, 0.25, 0.50, 0.75, 0.90)  taupoints(29) rhopoints(35)  meshsize(0.5) frank nostderrors  centergrid(-0.20)

matrix beta_boot = e(b)

forvalue i = 1/119 { 
    
    ereturn scalar beta_boot_`i' =beta_boot[1, `i']
}


**# Bookmark #1
restore 
end 

* step 3: run the bootstrap** 

simulate b_r11dentst_dental_insurance = beta_boot[1,1] b_r11dentst_endentulism = beta_boot[1,2] b_r11dentst_inc_d = beta_boot[1,3] b_r11dentst_race = beta_boot[1,4] b_r11dentst_age_cat = beta_boot[1,5] ///
b_r11dentst_male = beta_boot[1,6] b_r11dentst_education = beta_boot[1,7] b_r11dentst_veteran = beta_boot[1,8] b_r11dentst_mothered = beta_boot[1,9] b_r11dentst_wealth = beta_boot[1,10] ///
b_r11dentst_smoke_now = beta_boot[1,11] b_r11dentst_chronicdisease = beta_boot[1,12] b_r11dentst_cons = beta_boot[1,13] b_q10_cons = beta_boot[1,14] b_q10_inc_d = beta_boot[1,15] ///
b_q10_endentulism = beta_boot[1,16] b_q10_race_2 = beta_boot[1,17] b_q10_race_3 = beta_boot[1,18] b_q10_race_4 = beta_boot[1,19] b_q10_age_cat_2 = beta_boot[1,20] ///
b_q10_age_cat_3 = beta_boot[1,21] b_q10_age_cat_4 = beta_boot[1,22] b_q10_male = beta_boot[1,23] b_q10_education_2 = beta_boot[1,24] b_q10_education_3 = beta_boot[1,25] ///
b_q10_education_4 = beta_boot[1,26] b_q10_education_5 = beta_boot[1,27] b_q10_veteran = beta_boot[1,28] b_q10_mothered = beta_boot[1,29] b_q10_wealth_2 = beta_boot[1,30] ///
b_q10_wealth_3 = beta_boot[1,31] b_q10_wealth_4 = beta_boot[1,32] b_q10_smoke_now = beta_boot[1,33] b_q10_chronicdisease = beta_boot[1,34] b_q25_cons = beta_boot[1,35] ///
b_q25_inc_d = beta_boot[1,36] b_q25_endentulism = beta_boot[1,37] b_q25_race_2 = beta_boot[1,38] b_q25_race_3 = beta_boot[1,39] b_q25_race_4 = beta_boot[1,40] ///
b_q25_age_cat_2 = beta_boot[1,41] b_q25_age_cat_3 = beta_boot[1,42] b_q25_age_cat_4 = beta_boot[1,43] b_q25_male = beta_boot[1,44] b_q25_education_2 = beta_boot[1,45] ///
b_q25_education_3 = beta_boot[1,46] b_q25_education_4 = beta_boot[1,47] b_q25_education_5 = beta_boot[1,48] b_q25_veteran = beta_boot[1,49] b_q25_mothered = beta_boot[1,50] ///
b_q25_wealth_2 = beta_boot[1,51] b_q25_wealth_3 = beta_boot[1,52] b_q25_wealth_4 = beta_boot[1,53] b_q25_smoke_now = beta_boot[1,54] b_q25_chronicdisease = beta_boot[1,55] ///
b_q50_cons = beta_boot[1,56] b_q50_inc_d = beta_boot[1,57] b_q50_endentulism = beta_boot[1,58] b_q50_race_2 = beta_boot[1,59] b_q50_race_3 = beta_boot[1,60] ///
b_q50_race_4 = beta_boot[1,61] b_q50_age_cat_2 = beta_boot[1,62] b_q50_age_cat_3 = beta_boot[1,63] b_q50_age_cat_4 = beta_boot[1,64] b_q50_male = beta_boot[1,65] ///
b_q50_education_2 = beta_boot[1,66] b_q50_education_3 = beta_boot[1,67] b_q50_education_4 = beta_boot[1,68] b_q50_education_5 = beta_boot[1,69] b_q50_veteran = beta_boot[1,70] ///
b_q50_mothered = beta_boot[1,71] b_q50_wealth_2 = beta_boot[1,72] b_q50_wealth_3 = beta_boot[1,73] b_q50_wealth_4 = beta_boot[1,74] b_q50_smoke_now = beta_boot[1,75] ///
b_q50_chronicdisease = beta_boot[1,76] b_q75_cons = beta_boot[1,77] b_q75_inc_d = beta_boot[1,78] b_q75_endentulism = beta_boot[1,79] b_q75_race_2 = beta_boot[1,80] ///
b_q75_race_3 = beta_boot[1,81] b_q75_race_4 = beta_boot[1,82] b_q75_age_cat_2 = beta_boot[1,83] b_q75_age_cat_3 = beta_boot[1,84] b_q75_age_cat_4 = beta_boot[1,85] ///
b_q75_male = beta_boot[1,86] b_q75_education_2 = beta_boot[1,87] b_q75_education_3 = beta_boot[1,88] b_q75_education_4 = beta_boot[1,89] b_q75_education_5 = beta_boot[1,90] ///
b_q75_veteran = beta_boot[1,91] b_q75_mothered = beta_boot[1,92] b_q75_wealth_2 = beta_boot[1,93] b_q75_wealth_3 = beta_boot[1,94] b_q75_wealth_4 = beta_boot[1,95] ///
b_q75_smoke_now = beta_boot[1,96] b_q75_chronicdisease = beta_boot[1,97] b_q90_cons = beta_boot[1,98] b_q90_inc_d = beta_boot[1,99] b_q90_endentulism = beta_boot[1,100] ///
b_q90_race_2 = beta_boot[1,101] b_q90_race_3 = beta_boot[1,102] b_q90_race_4 = beta_boot[1,103] b_q90_age_cat_2 = beta_boot[1,104] b_q90_age_cat_3 = beta_boot[1,105] ///
b_q90_age_cat_4 = beta_boot[1,106] b_q90_male = beta_boot[1,107] b_q90_education_2 = beta_boot[1,108] b_q90_education_3 = beta_boot[1,109] b_q90_education_4 = beta_boot[1,110] ///
b_q90_education_5 = beta_boot[1,111] b_q90_veteran = beta_boot[1,112] b_q90_mothered = beta_boot[1,113] b_q90_wealth_2 = beta_boot[1,114] b_q90_wealth_3 = beta_boot[1,115] ///
b_q90_wealth_4 = beta_boot[1,116] b_q90_smoke_now = beta_boot[1,117] b_q90_chronicdisease = beta_boot[1,118] b_anc_rho = beta_boot[1,119], /// 
reps(50) seed(123456): arhomme_bootstrap



* step 4 estimate the boostrap SE** 
preserve 
bstat, stat(beta) n(${Ns})
restore