test-gigs.log

--------------------------------------------------------------------------------------------------------------
      name:  GIGS Testing
       log:  D:\Users\tadeo\Documents\gigs-stata\test-gigs.log
  log type:  text
 opened on:  17 Jan 2025, 08:58:04

. 
. //  1. Generate .dta files with standards using "z2v/c2v" conversions,
. //     and with .dta files to be tested against R dataframes
. local outputs "tests/outputs"

. cap mkdir "`outputs'"

. foreach aspect in "who_gs" "ig_nbs" "ig_nbs_ext" "ig_png" "ig_fet" ///
>     "interpolation" "z_lgls" "classification" {
  2.         cap mkdir "`outputs'/`aspect'"
  3.         noi di "Running .dta file generation for `aspect'"
  4.         frames reset
  5.         run "tests/test-`aspect'.do"
  6. }
Running .dta file generation for who_gs
Running .dta file generation for ig_nbs
Running .dta file generation for ig_nbs_ext
Running .dta file generation for ig_png
Running .dta file generation for ig_fet
Running .dta file generation for interpolation
Running .dta file generation for z_lgls
Running .dta file generation for classification

. 
. //  2. Compare to standards in gigs R package
. //              a. n.b. Install rsource (R from Stata) if not already installed:
. //                 . cap ssc install rsource, replace
. local test_rscript "tests/test_stata_outputs.R"

. if "`c(os)'"=="MacOSX" | "`c(os)'"=="UNIX" {
.     noi rsource using "`test_rscript'", ///
>                 noloutput ///
>                 rpath("/usr/local/bin/R") ///
>                 roptions(`"--vanilla"')
. }

. else if "`c(os)'"=="Windows" { 
.         // Windows
.         // n.b. Set `rversion' to that of own system - n.b. gigs needs R >=4.1.0
.         local rversion "4.3.3" 
.         noi rsource using "`test_rscript'", ///
>                 noloutput ///
>                 rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe") ///
>                 roptions("--vanilla")
Assumed R program path: "C:\Program Files\R\R-4.3.3\bin\x64\Rterm.exe"

── INTERGROWTH-21st Fetal Growth Standards ─────────────────────────────────────
✔ Centiles in hcfga: succeeded
✔ Z-scores in hcfga: succeeded
✔ Centiles in bpdfga: succeeded
✔ Z-scores in bpdfga: succeeded
✔ Centiles in acfga: succeeded
✔ Z-scores in acfga: succeeded
✔ Centiles in flfga: succeeded
✔ Z-scores in flfga: succeeded
✔ Centiles in ofdfga: succeeded
✔ Z-scores in ofdfga: succeeded
✔ Centiles in efwfga: succeeded
✔ Z-scores in efwfga: succeeded
✔ Centiles in sfhfga: succeeded
✔ Z-scores in sfhfga: succeeded
✔ Centiles in crlfga: succeeded
✔ Z-scores in crlfga: succeeded
✔ Centiles in gafcrl: succeeded
✔ Z-scores in gafcrl: succeeded
✔ Centiles in gwgfga: succeeded
✔ Z-scores in gwgfga: succeeded
✔ Centiles in pifga: succeeded
✔ Z-scores in pifga: succeeded
✔ Centiles in rifga: succeeded
✔ Z-scores in rifga: succeeded
✔ Centiles in sdrfga: succeeded
✔ Z-scores in sdrfga: succeeded
✔ Centiles in tcdfga: succeeded
✔ Z-scores in tcdfga: succeeded
✔ Centiles in gaftcd: succeeded
✔ Z-scores in gaftcd: succeeded
✔ Centiles in poffga: succeeded
✔ Z-scores in poffga: succeeded
✔ Centiles in sffga: succeeded
✔ Z-scores in sffga: succeeded
✔ Centiles in avfga: succeeded
✔ Z-scores in avfga: succeeded
✔ Centiles in pvfga: succeeded
✔ Z-scores in pvfga: succeeded
✔ Centiles in cmfga: succeeded
✔ Z-scores in cmfga: succeeded
✔ Centiles in hefwfga: succeeded
✔ Z-scores in hefwfga: succeeded

── INTERGROWTH-21st Newborn Size Standards ─────────────────────────────────────
✔ Male zscores in wfga: succeeded
✔ Male centiles in wfga: succeeded
✔ Female zscores in wfga: succeeded
✔ Female centiles in wfga: succeeded
✔ Male zscores in lfga: succeeded
✔ Male centiles in lfga: succeeded
✔ Female zscores in lfga: succeeded
✔ Female centiles in lfga: succeeded
✔ Male zscores in hcfga: succeeded
✔ Male centiles in hcfga: succeeded
✔ Female zscores in hcfga: succeeded
✔ Female centiles in hcfga: succeeded
✔ Male zscores in wlrfga: succeeded
✔ Male centiles in wlrfga: succeeded
✔ Female zscores in wlrfga: succeeded
✔ Female centiles in wlrfga: succeeded
ℹ File not found: 'tests/outputs/ig_nbs/ffmfga_z2v_male.dta'
✔ Male centiles in ffmfga: succeeded
ℹ File not found: 'tests/outputs/ig_nbs/ffmfga_z2v_female.dta'
✔ Female centiles in ffmfga: succeeded
ℹ File not found: 'tests/outputs/ig_nbs/bfpfga_z2v_male.dta'
✔ Male centiles in bfpfga: succeeded
ℹ File not found: 'tests/outputs/ig_nbs/bfpfga_z2v_female.dta'
✔ Female centiles in bfpfga: succeeded
ℹ File not found: 'tests/outputs/ig_nbs/fmfga_z2v_male.dta'
✔ Male centiles in fmfga: succeeded
ℹ File not found: 'tests/outputs/ig_nbs/fmfga_z2v_female.dta'
✔ Female centiles in fmfga: succeeded

── Extended INTERGROWTH-21st Newborn Size Standards ────────────────────────────
✔ Male zscores in wfga: succeeded
✔ Male centiles in wfga: succeeded
✔ Female zscores in wfga: succeeded
✔ Female centiles in wfga: succeeded
✔ Male zscores in lfga: succeeded
✔ Male centiles in lfga: succeeded
✔ Female zscores in lfga: succeeded
✔ Female centiles in lfga: succeeded
✔ Male zscores in hcfga: succeeded
✔ Male centiles in hcfga: succeeded
✔ Female zscores in hcfga: succeeded
✔ Female centiles in hcfga: succeeded

── INTERGROWTH-21st Postnatal Growth Standards ─────────────────────────────────
✔ Male centiles in wfa: succeeded
✔ Female centiles in wfa: succeeded
✔ Male centiles in lfa: succeeded
✔ Female centiles in lfa: succeeded
✔ Male centiles in hcfa: succeeded
✔ Female centiles in hcfa: succeeded
✔ Male centiles in wfl: succeeded
✔ Female centiles in wfl: succeeded

── WHO Child Growth Standards ──────────────────────────────────────────────────
✔ Male centiles in wfa: succeeded
✔ Female centiles in wfa: succeeded
✔ Male centiles in bfa: succeeded
✔ Female centiles in bfa: succeeded
✔ Male centiles in lhfa: succeeded
✔ Female centiles in lhfa: succeeded
✔ Male centiles in wfl: succeeded
✔ Female centiles in wfl: succeeded
✔ Male centiles in wfh: succeeded
✔ Female centiles in wfh: succeeded
✔ Male centiles in hcfa: succeeded
✔ Female centiles in hcfa: succeeded
✔ Male centiles in acfa: succeeded
✔ Female centiles in acfa: succeeded
✔ Male centiles in ssfa: succeeded
✔ Female centiles in ssfa: succeeded
✔ Male centiles in tsfa: succeeded
✔ Female centiles in tsfa: succeeded

── Interpolation of coefficients ───────────────────────────────────────────────
✔ Interpolation is consistent for males in WHO GS `wfa`.
✔ Interpolation is consistent for females in WHO GS `wfa`.
✔ Interpolation is consistent for males in WHO GS `bfa`.
✔ Interpolation is consistent for females in WHO GS `bfa`.
✔ Interpolation is consistent for males in WHO GS `lhfa`.
✔ Interpolation is consistent for females in WHO GS `lhfa`.
✔ Interpolation is consistent for males in WHO GS `wfl`.
✔ Interpolation is consistent for females in WHO GS `wfl`.
✔ Interpolation is consistent for males in WHO GS `wfh`.
✔ Interpolation is consistent for females in WHO GS `wfh`.
✔ Interpolation is consistent for males in WHO GS `hcfa`.
✔ Interpolation is consistent for females in WHO GS `hcfa`.
✔ Interpolation is consistent for males in WHO GS `acfa`.
✔ Interpolation is consistent for females in WHO GS `acfa`.
✔ Interpolation is consistent for males in WHO GS `ssfa`.
✔ Interpolation is consistent for females in WHO GS `ssfa`.
✔ Interpolation is consistent for males in WHO GS `tsfa`.
✔ Interpolation is consistent for females in WHO GS `tsfa`.
✔ Interpolation is consistent for males in IG NBS `wfga`.
✔ Interpolation is consistent for females in IG NBS `wfga`.
✔ Interpolation is consistent for males in IG NBS `lfga`.
✔ Interpolation is consistent for females in IG NBS `lfga`.
✔ Interpolation is consistent for males in IG NBS `hcfga`.
✔ Interpolation is consistent for females in IG NBS `hcfga`.

── GIGS z-scoring logicals ─────────────────────────────────────────────────────
✔ GIGS zscoring logicals are consistent.
Warning messages:
1: In compare_gigs_z_lgls() :
  There were 7 'at birth' observations where `age_days` > 0.5.
ℹ This occurred for IDs 83, 312, 377, 678, 736, 838, and 916.
2: In compare_gigs_z_lgls() :
  There was 1 birth observation where `gest_age` > 300. The WHO Growth standards
were used for this observation instead of the INTERGROWTH-21st Newborn Size
standards.
ℹ This occurred for ID 874.

── GIGS classification ─────────────────────────────────────────────────────────
! Unused factor levels kept after small vulnerable newborn categorisation:
"Term LGA".
! Unused factor levels kept after weight-for-age (underweight) categorisation:
"outlier".
✔ GIGS classification is consistent.
Warning message:
In eval(substitute(expr), data, enclos = parent.frame()) :
  There were 134 'at birth' observations where `age_days` > 0.5.
ℹ This occurred for IDs 2, 3, 6, 7, 12, 13, 14, 16, 20, 21, 22, 23, 24, …, 295,
  and 298.

── Overall ─────────────────────────────────────────────────────────────────────
✔ All tests passed!
. }

. 
. // Stop logging
. log close
no log file open
r(606);

end of do-file

r(606);

. do "D:\Users\tadeo\Documents\gigs-stata\benchmarking\benchmark.do"

. /* 
>   The benchmarking code here is used to compare and contrast different versions
>   of the Stata code with each other, and with R implementations. 
> 
>   The benchmarking table used is the same used to compare the R/SAS 
>   implementations of these growth standards against each other. The generation
>   process for this table can be checked out on the R package website in the 
>   benchmarking article 
>   (https://docs.ropensci.org/gigs/articles/benchmarking.html).
>   
>   It is assumed that a user running this script has gigs installed, either as a 
>   stable release or dev version from GitHub.
> */
. 
. clear all

. 
. local bench_folder = "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. log using "`bench_folder'\statabench.log", replace text nomsg
(file D:\Users\tadeo\Documents\gigs-stata\benchmarking\statabench.log not found)

. 
. // WHO Child Growth Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_who_gs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "Number of inputs: `i'"
  5.         bench, reps(25) restore last: ///
>                 qui egen double z_gigs = who_gs(y, "wfa", "v2z"), ///
>                         xvar(x) sex(sex) sexcode(m=M, f=F)
  6. }
Number of inputs: 1
Average over 25 runs: 0.010 seconds
Number of inputs: 10
Average over 25 runs: 0.009 seconds
Number of inputs: 100
Average over 25 runs: 0.010 seconds
Number of inputs: 500
Average over 25 runs: 0.010 seconds
Number of inputs: 1000
Average over 25 runs: 0.012 seconds
Number of inputs: 5000
Average over 25 runs: 0.025 seconds
Number of inputs: 10000
Average over 25 runs: 0.041 seconds
Number of inputs: 25000
Average over 25 runs: 0.088 seconds
Number of inputs: 50000
Average over 25 runs: 0.169 seconds
Number of inputs: 75000
Average over 25 runs: 0.256 seconds
Number of inputs: 100000
Average over 25 runs: 0.351 seconds

. 
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_who_gs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "Number of inputs: `i'"
  5.         bench, reps(25) restore last: ///
>                 qui egen z_anthro = zanthro(y, wa, WHO), xvar(x) gender(sex) ///
>                         gencode(male=M, female=F) ageunit(day)
  6. }
Number of inputs: 1
Average over 25 runs: 0.008 seconds
Number of inputs: 10
Average over 25 runs: 0.008 seconds
Number of inputs: 100
Average over 25 runs: 0.009 seconds
Number of inputs: 500
Average over 25 runs: 0.013 seconds
Number of inputs: 1000
Average over 25 runs: 0.018 seconds
Number of inputs: 5000
Average over 25 runs: 0.060 seconds
Number of inputs: 10000
Average over 25 runs: 0.111 seconds
Number of inputs: 25000
Average over 25 runs: 0.268 seconds
Number of inputs: 50000
Average over 25 runs: 0.541 seconds
Number of inputs: 75000
Average over 25 runs: 1.243 seconds
Number of inputs: 100000
Average over 25 runs: 1.349 seconds

. 
. // IG-21st Newborn Size Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_nbs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "Number of inputs: `i'"
  5.         bench, reps(25) restore last: ///
>                 qui egen double z_gigs = ig_nbs(y, "wfga", "v2z"), ///
>                         gest_days(gest_days) sex(sex) sexcode(m=M, f=F)
  6. }
Number of inputs: 1
variable gest_days not found
(error in option gest_days())
r(111);

end of do-file

r(111);

. use life6mo
no; dataset in memory has changed since last saved
r(4);

. log close

. di "`bench_folder'\statabench.log"
\statabench.log

. cd
D:\Users\tadeo\Documents\gigs-stata

. 

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_000001.tmp"

. local bench_folder "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. 
end of do-file

. di "`bench_folder'"


. local bench_folder = "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. di "`bench_folder'"
D:\Users\tadeo\Documents\gigs-stata\benchmarking

. 

. do "D:\Users\tadeo\Documents\gigs-stata\benchmarking\benchmark.do"

. /* 
>   The benchmarking code here is used to compare and contrast different versions
>   of the Stata code with each other, and with R implementations. 
> 
>   The benchmarking table used is the same used to compare the R/SAS 
>   implementations of these growth standards against each other. The generation
>   process for this table can be checked out on the R package website in the 
>   benchmarking article 
>   (https://docs.ropensci.org/gigs/articles/benchmarking.html).
>   
>   It is assumed that a user running this script has gigs installed, either as a 
>   stable release or dev version from GitHub.
> */
. 
. clear all

. 
. local bench_folder "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. log using "`bench_folder'\statabench.log", replace text nomsg

. 
. // WHO Child Growth Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_who_gs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "Number of inputs: `i'"
  5.         bench, reps(50) restore last: ///
>                 qui egen double z_gigs = who_gs(y, "wfa", "v2z"), ///
>                         xvar(x) sex(sex) sexcode(m=M, f=F)
  6. }
Number of inputs: 1
Average over 50 runs: 0.009 seconds
Number of inputs: 10
Average over 50 runs: 0.009 seconds
Number of inputs: 100
Average over 50 runs: 0.009 seconds
Number of inputs: 500
Average over 50 runs: 0.010 seconds
Number of inputs: 1000
Average over 50 runs: 0.011 seconds
Number of inputs: 5000
Average over 50 runs: 0.024 seconds
Number of inputs: 10000
Average over 50 runs: 0.040 seconds
Number of inputs: 25000
Average over 50 runs: 0.089 seconds
Number of inputs: 50000
Average over 50 runs: 0.171 seconds
Number of inputs: 75000
Average over 50 runs: 0.257 seconds
Number of inputs: 100000
Average over 50 runs: 0.349 seconds

. 
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_who_gs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "Number of inputs: `i'"
  5.         bench, reps(50) restore last: ///
>                 qui egen z_anthro = zanthro(y, wa, WHO), xvar(x) gender(sex) ///
>                         gencode(male=M, female=F) ageunit(day)
  6. }
Number of inputs: 1
Average over 50 runs: 0.008 seconds
Number of inputs: 10
Average over 50 runs: 0.008 seconds
Number of inputs: 100
Average over 50 runs: 0.009 seconds
Number of inputs: 500
Average over 50 runs: 0.013 seconds
Number of inputs: 1000
Average over 50 runs: 0.018 seconds
Number of inputs: 5000
Average over 50 runs: 0.060 seconds
Number of inputs: 10000
Average over 50 runs: 0.114 seconds
Number of inputs: 25000
Average over 50 runs: 0.279 seconds
Number of inputs: 50000
Average over 50 runs: 0.545 seconds
Number of inputs: 75000
Average over 50 runs: 0.800 seconds
Number of inputs: 100000
Average over 50 runs: 1.104 seconds

. 
. // IG-21st Newborn Size Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_nbs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "Number of inputs: `i'"
  5.         bench, reps(50) restore last: ///
>                 qui egen double z_gigs = ig_nbs(y, "wfga", "v2z"), ///
>                         gest_days(x) sex(sex) sexcode(m=M, f=F)
  6. }
Number of inputs: 1
Average over 50 runs: 0.004 seconds
Number of inputs: 10
Average over 50 runs: 0.004 seconds
Number of inputs: 100
Average over 50 runs: 0.005 seconds
Number of inputs: 500
Average over 50 runs: 0.006 seconds
Number of inputs: 1000
Average over 50 runs: 0.007 seconds
Number of inputs: 5000
Average over 50 runs: 0.022 seconds
Number of inputs: 10000
Average over 50 runs: 0.041 seconds
Number of inputs: 25000
Average over 50 runs: 0.095 seconds
Number of inputs: 50000
Average over 50 runs: 0.187 seconds
Number of inputs: 75000
Average over 50 runs: 0.282 seconds
Number of inputs: 100000
Average over 50 runs: 0.383 seconds

. 
. // IG-21st Postnatal Growth Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_png.dta", clear
  3.         qui drop if _n > `i'
  4.         di "Number of inputs: `i'"
  5.         bench, reps(50) restore last: ///
>                 qui egen double z_gigs = ig_png(y, "wfa", "v2z"), ///
>                         xvar(x) sex(sex) sexcode(m=M, f=F)
  6. }
Number of inputs: 1
Average over 50 runs: 0.002 seconds
Number of inputs: 10
Average over 50 runs: 0.003 seconds
Number of inputs: 100
Average over 50 runs: 0.003 seconds
Number of inputs: 500
Average over 50 runs: 0.003 seconds
Number of inputs: 1000
Average over 50 runs: 0.003 seconds
Number of inputs: 5000
Average over 50 runs: 0.008 seconds
Number of inputs: 10000
Average over 50 runs: 0.014 seconds
Number of inputs: 25000
Average over 50 runs: 0.030 seconds
Number of inputs: 50000
Average over 50 runs: 0.057 seconds
Number of inputs: 75000
Average over 50 runs: 0.081 seconds
Number of inputs: 100000
Average over 50 runs: 0.109 seconds

. 
. // IG-21st Fetal Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_fet.dta", clear
  3.         qui drop if _n > `i'
  4.         di "Number of inputs: `i'"
  5.         bench, reps(50) restore last: ///
>                 qui egen double z_gigs = ig_fet(y, "ofdfga", "v2z"), xvar(x)
  6. }
Number of inputs: 1
Average over 50 runs: 0.002 seconds
Number of inputs: 10
Average over 50 runs: 0.002 seconds
Number of inputs: 100
Average over 50 runs: 0.002 seconds
Number of inputs: 500
Average over 50 runs: 0.003 seconds
Number of inputs: 1000
Average over 50 runs: 0.003 seconds
Number of inputs: 5000
Average over 50 runs: 0.005 seconds
Number of inputs: 10000
Average over 50 runs: 0.008 seconds
Number of inputs: 25000
Average over 50 runs: 0.017 seconds
Number of inputs: 50000
Average over 50 runs: 0.030 seconds
Number of inputs: 75000
Average over 50 runs: 0.042 seconds
Number of inputs: 100000
Average over 50 runs: 0.058 seconds

. 
. log close

. 
. 
. 
. // OLD VERSIONS OF GIGS --------------------------------------------------------
. // Kept to note how development is progressing
. 
. 
. 
. // gigs 0.4.0
. // Number of inputs: 1
. // Average over 25 runs: 0.009 seconds
. // Number of inputs: 10
. // Average over 25 runs: 0.008 seconds
. // Number of inputs: 100
. // Average over 25 runs: 0.009 seconds
. // Number of inputs: 500
. // Average over 25 runs: 0.010 seconds
. // Number of inputs: 1000
. // Average over 25 runs: 0.012 seconds
. // Number of inputs: 5000
. // Average over 25 runs: 0.028 seconds
. // Number of inputs: 10000
. // Average over 25 runs: 0.047 seconds
. // Number of inputs: 25000
. // Average over 25 runs: 0.104 seconds
. // Number of inputs: 50000
. // Average over 25 runs: 0.198 seconds
. // Number of inputs: 75000
. // Average over 25 runs: 0.296 seconds
. // Number of inputs: 100000
. // Average over 25 runs: 0.405 seconds
. 
. // gigs 0.3.2:
. // Number of inputs: 1
. // Average over 25 runs: 0.008 seconds
. // Number of inputs: 10
. // Average over 25 runs: 0.009 seconds
. // Number of inputs: 100
. // Average over 25 runs: 0.009 seconds
. // Number of inputs: 500
. // Average over 25 runs: 0.010 seconds
. // Number of inputs: 1000
. // Average over 25 runs: 0.012 seconds
. // Number of inputs: 5000
. // Average over 25 runs: 0.027 seconds
. // Number of inputs: 10000
. // Average over 25 runs: 0.045 seconds
. // Number of inputs: 25000
. // Average over 25 runs: 0.101 seconds
. // Number of inputs: 50000
. // Average over 25 runs: 0.195 seconds
. // Number of inputs: 75000
. // Average over 25 runs: 0.294 seconds
. // Number of inputs: 100000
. // Average over 25 runs: 0.405 seconds
. 
. // gigs 0.3.1:
. // Number of inputs: 1
. // Average over 25 runs: 0.008 seconds
. // Number of inputs: 10
. // Average over 25 runs: 0.009 seconds
. // Number of inputs: 100
. // Average over 25 runs: 0.009 seconds
. // Number of inputs: 500
. // Average over 25 runs: 0.010 seconds
. // Number of inputs: 1000
. // Average over 25 runs: 0.012 seconds
. // Number of inputs: 5000
. // Average over 25 runs: 0.028 seconds
. // Number of inputs: 10000
. // Average over 25 runs: 0.047 seconds
. // Number of inputs: 25000
. // Average over 25 runs: 0.106 seconds
. // Number of inputs: 50000
. // Average over 25 runs: 0.204 seconds
. // Number of inputs: 75000
. // Average over 25 runs: 0.310 seconds
. // Number of inputs: 100000
. // Average over 25 runs: 0.410 seconds
. 
. // gigs 0.3.0:
. // Number of inputs: 1
. // Average over 25 runs: 0.008 seconds
. // Number of inputs: 10
. // Average over 25 runs: 0.010 seconds
. // Number of inputs: 100
. // Average over 25 runs: 0.010 seconds
. // Number of inputs: 500
. // Average over 25 runs: 0.011 seconds
. // Number of inputs: 1000
. // Average over 25 runs: 0.013 seconds
. // Number of inputs: 5000
. // Average over 25 runs: 0.028 seconds
. // Number of inputs: 10000
. // Average over 25 runs: 0.048 seconds
. // Number of inputs: 25000
. // Average over 25 runs: 0.107 seconds
. // Number of inputs: 50000
. // Average over 25 runs: 0.205 seconds
. // Number of inputs: 75000
. // Average over 25 runs: 0.313 seconds
. // Number of inputs: 100000
. // Average over 25 runs: 0.431 seconds
. 
. // gigs 0.2.4:
. // Number of inputs: 1
. // Average over 25 runs: 0.016 seconds
. // Number of inputs: 10
. // Average over 25 runs: 0.015 seconds
. // Number of inputs: 100
. // Average over 25 runs: 0.017 seconds
. // Number of inputs: 500
. // Average over 25 runs: 0.039 seconds
. // Number of inputs: 1000
. // Average over 25 runs: 0.079 seconds
. // Number of inputs: 5000
. // Average over 25 runs: 1.007 seconds
. // --> Crashed on 10000 or more inputs
. 
end of do-file

. clear all

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_000002.tmp"

. local bench_folder "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. 
end of do-file

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_000003.tmp"

. clear all

. 
. local bench_folder "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. 
end of do-file

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_000004.tmp"

. local benchmarking_rscript "`bench_folder'\statabench2csv.R"

. if "`c(os)'"=="MacOSX" | "`c(os)'"=="UNIX" {
.     noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("/usr/local/bin/R") ///
>                 roptions(`"--vanilla"')
. }

. else if "`c(os)'"=="Windows" { 
.         // Windows
.         // n.b. Set `rversion' to that of own system - n.b. gigs needs R >=4.1.0
.         local rversion "4.3.3" 
.         noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe") ///
>                 roptions("--vanilla")
file \statabench2csv.R not found
r(601);
. }
r(601);

end of do-file

r(601);

. di "`benchmarking_rscript'"


. local benchmarking_rscript = "`bench_folder'\statabench2csv.R"

. di "`benchmarking_rscript'"
D:\Users\tadeo\Documents\gigs-stata\benchmarking\statabench2csv.R

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_000005.tmp"

. local benchmarking_rscript = "`bench_folder'\statabench2csv.R"

. if "`c(os)'"=="MacOSX" | "`c(os)'"=="UNIX" {
.     noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("/usr/local/bin/R") ///
>                 roptions(`"--vanilla"')
. }

. else if "`c(os)'"=="Windows" { 
.         // Windows
.         // n.b. Set `rversion' to that of own system - n.b. gigs needs R >=4.1.0
.         local rversion "4.3.3" 
.         noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe") ///
>                 roptions("--vanilla")
file \statabench2csv.R not found
r(601);
. }
r(601);

end of do-file

r(601);

. di "`benchmarking_rscript'"
D:\Users\tadeo\Documents\gigs-stata\benchmarking\statabench2csv.R

. set trace on

. "`benchmarking_rscript'"
"D:\Users\tadeo\Documents\gigs-stata\benchmarking\statabench2csv.R is not a valid command name
r(199);

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_000006.tmp"

. if "`c(os)'"=="MacOSX" | "`c(os)'"=="UNIX" {
.     noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("/usr/local/bin/R") ///
>                 roptions(`"--vanilla"')
. }

. else if "`c(os)'"=="Windows" { 
.         // Windows
.         // n.b. Set `rversion' to that of own system - n.b. gigs needs R >=4.1.0
.         local rversion "4.3.3" 
.         noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe") ///
>                 roptions("--vanilla")
  --------------------------------------------------------------------------------------------------------------------------- begin rsource ---
  - version 10.0
  - syntax [ using/ ] [ , TErminator(string) MAXlines(numlist min=1 max=1 >=0) RPath(string) ROptions(string) noLOutput LSource ]
invalid file specification
  ----------------------------------------------------------------------------------------------------------------------------- end rsource ---
r(198);
. }
r(198);

end of do-file

r(198);

. set trace off

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_000007.tmp"

. if "`c(os)'"=="MacOSX" | "`c(os)'"=="UNIX" {
.     noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("/usr/local/bin/R") ///
>                 roptions(`"--vanilla"')
. }

. else if "`c(os)'"=="Windows" { 
.         // Windows
.         // n.b. Set `rversion' to that of own system - n.b. gigs needs R >=4.1.0
.         local rversion "4.3.3" 
.         noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe") ///
>                 roptions("--vanilla")
invalid file specification
r(198);
. }
r(198);

end of do-file

r(198);

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_000008.tmp"

.         noi rsource using "`benchmarking_rscript'", ///

end of do-file

. noi rsource using "`benchmarking_rscript'", noloutput  rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe")  roptions("--vanilla")
Assumed R program path: "C:\Program Files\R\R-\bin\x64\Rterm.exe"
The system cannot find the path specified.

. local rversion "4.3.3"

. noi rsource using "`benchmarking_rscript'", noloutput  rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe")  roptions("--vanilla")
Assumed R program path: "C:\Program Files\R\R-4.3.3\bin\x64\Rterm.exe"
Error in `map()`:
ℹ In index: 1.
Caused by error in `data.frame()`:
! arguments imply differing number of rows: 0, 1
Backtrace:
     ▆
  1. ├─purrr::map_dfr(...)
  2. │ └─purrr::map(.x, .f, ...)
  3. │   └─purrr:::map_("list", .x, .f, ..., .progress = .progress)
  4. │     ├─purrr:::with_indexed_errors(...)
  5. │     │ └─base::withCallingHandlers(...)
  6. │     ├─purrr:::call_with_cleanup(...)
  7. │     └─global .f(.x[[i]], ...)
  8. │       └─global logfile_to_df(logfile, acronym)
  9. │         └─base::data.frame(...)
 10. │           └─base::stop(...)
 11. └─base::.handleSimpleError(...)
 12.   └─purrr (local) h(simpleError(msg, call))
 13.     └─cli::cli_abort(...)
 14.       └─rlang::abort(...)
Execution halted

. noi rsource using "`benchmarking_rscript'", noloutput  rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe")  roptions("--vanilla")
Assumed R program path: "C:\Program Files\R\R-4.3.3\bin\x64\Rterm.exe"
Error in `map()`:
ℹ In index: 1.
Caused by error in `data.frame()`:
! arguments imply differing number of rows: 0, 1
Backtrace:
     ▆
  1. ├─purrr::map_dfr(...)
  2. │ └─purrr::map(.x, .f, ...)
  3. │   └─purrr:::map_("list", .x, .f, ..., .progress = .progress)
  4. │     ├─purrr:::with_indexed_errors(...)
  5. │     │ └─base::withCallingHandlers(...)
  6. │     ├─purrr:::call_with_cleanup(...)
  7. │     └─global .f(.x[[i]], ...)
  8. │       └─global logfile_to_df(logfile, acronym)
  9. │         └─base::data.frame(...)
 10. │           └─base::stop(...)
 11. └─base::.handleSimpleError(...)
 12.   └─purrr (local) h(simpleError(msg, call))
 13.     └─cli::cli_abort(...)
 14.       └─rlang::abort(...)
Execution halted

. noi rsource using "`benchmarking_rscript'", noloutput  rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe")  roptions("--vanilla")
Assumed R program path: "C:\Program Files\R\R-4.3.3\bin\x64\Rterm.exe"
→ D:/Users/tadeo/Documents/gigs-stata
Error in `map()`:
ℹ In index: 1.
Caused by error in `data.frame()`:
! arguments imply differing number of rows: 0, 1
Backtrace:
     ▆
  1. ├─purrr::map_dfr(...)
  2. │ └─purrr::map(.x, .f, ...)
  3. │   └─purrr:::map_("list", .x, .f, ..., .progress = .progress)
  4. │     ├─purrr:::with_indexed_errors(...)
  5. │     │ └─base::withCallingHandlers(...)
  6. │     ├─purrr:::call_with_cleanup(...)
  7. │     └─global .f(.x[[i]], ...)
  8. │       └─global logfile_to_df(logfile, acronym)
  9. │         └─base::data.frame(...)
 10. │           └─base::stop(...)
 11. └─base::.handleSimpleError(...)
 12.   └─purrr (local) h(simpleError(msg, call))
 13.     └─cli::cli_abort(...)
 14.       └─rlang::abort(...)
Execution halted

. noi rsource using "`benchmarking_rscript'", noloutput  rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe")  roptions("--vanilla")
Assumed R program path: "C:\Program Files\R\R-4.3.3\bin\x64\Rterm.exe"
Error in `map()`:
ℹ In index: 1.
Caused by error in `data.frame()`:
! arguments imply differing number of rows: 0, 1
Backtrace:
     ▆
  1. ├─purrr::map_dfr(...)
  2. │ └─purrr::map(.x, .f, ...)
  3. │   └─purrr:::map_("list", .x, .f, ..., .progress = .progress)
  4. │     ├─purrr:::with_indexed_errors(...)
  5. │     │ └─base::withCallingHandlers(...)
  6. │     ├─purrr:::call_with_cleanup(...)
  7. │     └─global .f(.x[[i]], ...)
  8. │       └─global logfile_to_df(logfile, acronym)
  9. │         └─base::data.frame(...)
 10. │           └─base::stop(...)
 11. └─base::.handleSimpleError(...)
 12.   └─purrr (local) h(simpleError(msg, call))
 13.     └─cli::cli_abort(...)
 14.       └─rlang::abort(...)
Execution halted

. noi rsource using "`benchmarking_rscript'", noloutput  rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe")  roptions("--vanilla")
Assumed R program path: "C:\Program Files\R\R-4.3.3\bin\x64\Rterm.exe"
Warning message:
. . // WHO Child Growth Standards. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {  2.         use "`bench_folder'/bench_d
> s_who_gs.dta", clear  3.         qui drop if _n > `i'  4.         di "Number of inputs: `i'"  5.         bench, reps(50) restore last: ///>  
>                qui egen double z_gigs = who_gs(y, "wfa", "v2z"), ///>                         xvar(x) sex(sex) sexcode(m=M, f=F)  6. }Number 
> of inputs: 1Average over 50 runs: 0.009 secondsNumber of inputs: 10Average over 50 runs: 0.009 secondsNumber of inputs: 100Average over 50 ru
> ns: 0.009 secondsNumber of inputs: 500Average over 50 runs: 0.010 secondsNumber of inputs: 1000Average over 50 runs: 0.011 secondsNumber of i
> nputs: 5000Average over 50 runs: 0.024 secondsNumber of inputs: 10000Average over 50 runs: 0.040 secondsNumber of inputs: 25000Average over 5
> 0 runs: 0.089 secondsNumber of inputs: 50000Average over 50 runs: 0.171 secondsNumber of inputs: 75000Average over 50 runs: 0.257 secondsNumb
> er of input [... truncated] 
Error in `map()`:
ℹ In index: 1.
Caused by error in `data.frame()`:
! arguments imply differing number of rows: 0, 1
Backtrace:
     ▆
  1. ├─purrr::map_dfr(...)
  2. │ └─purrr::map(.x, .f, ...)
  3. │   └─purrr:::map_("list", .x, .f, ..., .progress = .progress)
  4. │     ├─purrr:::with_indexed_errors(...)
  5. │     │ └─base::withCallingHandlers(...)
  6. │     ├─purrr:::call_with_cleanup(...)
  7. │     └─global .f(.x[[i]], ...)
  8. │       └─global logfile_to_df(logfile, acronym)
  9. │         └─base::data.frame(...)
 10. │           └─base::stop(...)
 11. └─base::.handleSimpleError(...)
 12.   └─purrr (local) h(simpleError(msg, call))
 13.     └─cli::cli_abort(...)
 14.       └─rlang::abort(...)
Execution halted

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_000009.tmp"

. clear all

. 
. local bench_folder "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. log using "`bench_folder'\statabench.log", replace text nomsg

. 
. // WHO Child Growth Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_who_gs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For who_gs: Number of inputs: `i'"
  5.         bench, reps(50) restore last: ///
>                 qui egen double z_gigs = who_gs(y, "wfa", "v2z"), ///
>                         xvar(x) sex(sex) sexcode(m=M, f=F)
  6. }
For who_gs: Number of inputs: 1
Average over 50 runs: 0.008 seconds
For who_gs: Number of inputs: 10
Average over 50 runs: 0.009 seconds
For who_gs: Number of inputs: 100
Average over 50 runs: 0.009 seconds
For who_gs: Number of inputs: 500
Average over 50 runs: 0.010 seconds
For who_gs: Number of inputs: 1000
Average over 50 runs: 0.012 seconds
For who_gs: Number of inputs: 5000
Average over 50 runs: 0.024 seconds
For who_gs: Number of inputs: 10000
Average over 50 runs: 0.040 seconds
For who_gs: Number of inputs: 25000
--Break--
r(1);

end of do-file

--Break--
r(1);

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_00000a.tmp"

. clear all

. 
. local bench_folder "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. log using "`bench_folder'\statabench.log", replace text nomsg
log file already open
r(604);

end of do-file

r(604);

. log close

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_00000a.tmp"

. clear all

. 
. local bench_folder "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. log using "`bench_folder'\statabench.log", replace text nomsg

. 
. // WHO Child Growth Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_who_gs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For who_gs: Number of inputs: `i'"
  5.         bench, reps(1) restore last: ///
>                 qui egen double z_gigs = who_gs(y, "wfa", "v2z"), ///
>                         xvar(x) sex(sex) sexcode(m=M, f=F)
  6. }
For who_gs: Number of inputs: 1
0.077 seconds
For who_gs: Number of inputs: 10
0.009 seconds
For who_gs: Number of inputs: 100
0.011 seconds
For who_gs: Number of inputs: 500
0.009 seconds
For who_gs: Number of inputs: 1000
0.012 seconds
For who_gs: Number of inputs: 5000
0.024 seconds
For who_gs: Number of inputs: 10000
0.040 seconds
For who_gs: Number of inputs: 25000
0.095 seconds
For who_gs: Number of inputs: 50000
0.185 seconds
For who_gs: Number of inputs: 75000
0.282 seconds
For who_gs: Number of inputs: 100000
0.379 seconds

. 
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_who_gs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For zanthro: Number of inputs: `i'"
  5.         bench, reps(1) restore last: ///
>                 qui egen z_anthro = zanthro(y, wa, WHO), xvar(x) gender(sex) ///
>                         gencode(male=M, female=F) ageunit(day)
  6. }
For zanthro: Number of inputs: 1
0.012 seconds
For zanthro: Number of inputs: 10
0.015 seconds
For zanthro: Number of inputs: 100
0.009 seconds
For zanthro: Number of inputs: 500
0.014 seconds
For zanthro: Number of inputs: 1000
0.028 seconds
For zanthro: Number of inputs: 5000
0.073 seconds
For zanthro: Number of inputs: 10000
0.125 seconds
For zanthro: Number of inputs: 25000
0.277 seconds
For zanthro: Number of inputs: 50000
0.537 seconds
For zanthro: Number of inputs: 75000
0.807 seconds
For zanthro: Number of inputs: 100000
1.070 seconds

. 
. // IG-21st Newborn Size Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_nbs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For ig_nbs: Number of inputs: `i'"
  5.         bench, reps(1) restore last: ///
>                 qui egen double z_gigs = ig_nbs(y, "wfga", "v2z"), ///
>                         gest_days(x) sex(sex) sexcode(m=M, f=F)
  6. }
For ig_nbs: Number of inputs: 1
0.004 seconds
For ig_nbs: Number of inputs: 10
0.004 seconds
For ig_nbs: Number of inputs: 100
0.004 seconds
For ig_nbs: Number of inputs: 500
0.006 seconds
For ig_nbs: Number of inputs: 1000
0.007 seconds
For ig_nbs: Number of inputs: 5000
0.022 seconds
For ig_nbs: Number of inputs: 10000
0.048 seconds
For ig_nbs: Number of inputs: 25000
0.102 seconds
For ig_nbs: Number of inputs: 50000
0.195 seconds
For ig_nbs: Number of inputs: 75000
0.288 seconds
For ig_nbs: Number of inputs: 100000
0.391 seconds

. 
. // IG-21st Postnatal Growth Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_png.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For ig_png: Number of inputs: `i'"
  5.         bench, reps(1) restore last: ///
>                 qui egen double z_gigs = ig_png(y, "wfa", "v2z"), ///
>                         xvar(x) sex(sex) sexcode(m=M, f=F)
  6. }
For ig_png: Number of inputs: 1
0.003 seconds
For ig_png: Number of inputs: 10
0.002 seconds
For ig_png: Number of inputs: 100
0.002 seconds
For ig_png: Number of inputs: 500
0.003 seconds
For ig_png: Number of inputs: 1000
0.004 seconds
For ig_png: Number of inputs: 5000
0.008 seconds
For ig_png: Number of inputs: 10000
0.014 seconds
For ig_png: Number of inputs: 25000
0.030 seconds
For ig_png: Number of inputs: 50000
0.063 seconds
For ig_png: Number of inputs: 75000
0.089 seconds
For ig_png: Number of inputs: 100000
0.116 seconds

. 
. // IG-21st Fetal Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_fet.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For ig_fet: Number of inputs: `i'"
  5.         bench, reps(1) restore last: ///
>                 qui egen double z_gigs = ig_fet(y, "ofdfga", "v2z"), xvar(x)
  6. }
For ig_fet: Number of inputs: 1
0.003 seconds
For ig_fet: Number of inputs: 10
0.002 seconds
For ig_fet: Number of inputs: 100
0.003 seconds
For ig_fet: Number of inputs: 500
0.003 seconds
For ig_fet: Number of inputs: 1000
0.003 seconds
For ig_fet: Number of inputs: 5000
0.005 seconds
For ig_fet: Number of inputs: 10000
0.007 seconds
For ig_fet: Number of inputs: 25000
0.017 seconds
For ig_fet: Number of inputs: 50000
0.038 seconds
For ig_fet: Number of inputs: 75000
0.042 seconds
For ig_fet: Number of inputs: 100000
0.067 seconds

. 
. log close

. 
end of do-file

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_00000b.tmp"

. clear all

. 
. local bench_folder "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. log using "`bench_folder'\statabench.log", replace text nomsg

. 
. // WHO Child Growth Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_who_gs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For who_gs: Number of inputs: `i'"
  5.         bench, reps(5) restore last: ///
>                 qui egen double z_gigs = who_gs(y, "wfa", "v2z"), ///
>                         xvar(x) sex(sex) sexcode(m=M, f=F)
  6. }
For who_gs: Number of inputs: 1
Average over 5 runs: 0.021 seconds
For who_gs: Number of inputs: 10
Average over 5 runs: 0.012 seconds
For who_gs: Number of inputs: 100
Average over 5 runs: 0.011 seconds
For who_gs: Number of inputs: 500
Average over 5 runs: 0.010 seconds
For who_gs: Number of inputs: 1000
Average over 5 runs: 0.013 seconds
For who_gs: Number of inputs: 5000
Average over 5 runs: 0.028 seconds
For who_gs: Number of inputs: 10000
Average over 5 runs: 0.043 seconds
For who_gs: Number of inputs: 25000
Average over 5 runs: 0.092 seconds
For who_gs: Number of inputs: 50000
Average over 5 runs: 0.175 seconds
For who_gs: Number of inputs: 75000
Average over 5 runs: 0.268 seconds
For who_gs: Number of inputs: 100000
Average over 5 runs: 0.357 seconds

. 
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_who_gs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For zanthro: Number of inputs: `i'"
  5.         bench, reps(5) restore last: ///
>                 qui egen z_anthro = zanthro(y, wa, WHO), xvar(x) gender(sex) ///
>                         gencode(male=M, female=F) ageunit(day)
  6. }
For zanthro: Number of inputs: 1
Average over 5 runs: 0.010 seconds
For zanthro: Number of inputs: 10
Average over 5 runs: 0.010 seconds
For zanthro: Number of inputs: 100
Average over 5 runs: 0.010 seconds
For zanthro: Number of inputs: 500
Average over 5 runs: 0.014 seconds
For zanthro: Number of inputs: 1000
Average over 5 runs: 0.020 seconds
For zanthro: Number of inputs: 5000
Average over 5 runs: 0.061 seconds
For zanthro: Number of inputs: 10000
Average over 5 runs: 0.113 seconds
For zanthro: Number of inputs: 25000
Average over 5 runs: 0.276 seconds
For zanthro: Number of inputs: 50000
Average over 5 runs: 0.547 seconds
For zanthro: Number of inputs: 75000
Average over 5 runs: 0.799 seconds
For zanthro: Number of inputs: 100000
Average over 5 runs: 1.066 seconds

. 
. // IG-21st Newborn Size Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_nbs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For ig_nbs: Number of inputs: `i'"
  5.         bench, reps(5) restore last: ///
>                 qui egen double z_gigs = ig_nbs(y, "wfga", "v2z"), ///
>                         gest_days(x) sex(sex) sexcode(m=M, f=F)
  6. }
For ig_nbs: Number of inputs: 1
Average over 5 runs: 0.003 seconds
For ig_nbs: Number of inputs: 10
Average over 5 runs: 0.006 seconds
For ig_nbs: Number of inputs: 100
Average over 5 runs: 0.004 seconds
For ig_nbs: Number of inputs: 500
Average over 5 runs: 0.006 seconds
For ig_nbs: Number of inputs: 1000
Average over 5 runs: 0.007 seconds
For ig_nbs: Number of inputs: 5000
Average over 5 runs: 0.022 seconds
For ig_nbs: Number of inputs: 10000
Average over 5 runs: 0.040 seconds
For ig_nbs: Number of inputs: 25000
Average over 5 runs: 0.096 seconds
For ig_nbs: Number of inputs: 50000
Average over 5 runs: 0.185 seconds
For ig_nbs: Number of inputs: 75000
Average over 5 runs: 0.280 seconds
For ig_nbs: Number of inputs: 100000
Average over 5 runs: 0.387 seconds

. 
. // IG-21st Postnatal Growth Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_png.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For ig_png: Number of inputs: `i'"
  5.         bench, reps(5) restore last: ///
>                 qui egen double z_gigs = ig_png(y, "wfa", "v2z"), ///
>                         xvar(x) sex(sex) sexcode(m=M, f=F)
  6. }
For ig_png: Number of inputs: 1
Average over 5 runs: 0.002 seconds
For ig_png: Number of inputs: 10
Average over 5 runs: 0.004 seconds
For ig_png: Number of inputs: 100
Average over 5 runs: 0.002 seconds
For ig_png: Number of inputs: 500
Average over 5 runs: 0.005 seconds
For ig_png: Number of inputs: 1000
Average over 5 runs: 0.003 seconds
For ig_png: Number of inputs: 5000
Average over 5 runs: 0.008 seconds
For ig_png: Number of inputs: 10000
Average over 5 runs: 0.013 seconds
For ig_png: Number of inputs: 25000
Average over 5 runs: 0.032 seconds
For ig_png: Number of inputs: 50000
Average over 5 runs: 0.056 seconds
For ig_png: Number of inputs: 75000
Average over 5 runs: 0.082 seconds
For ig_png: Number of inputs: 100000
Average over 5 runs: 0.110 seconds

. 
. // IG-21st Fetal Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_fet.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For ig_fet: Number of inputs: `i'"
  5.         bench, reps(5) restore last: ///
>                 qui egen double z_gigs = ig_fet(y, "ofdfga", "v2z"), xvar(x)
  6. }
For ig_fet: Number of inputs: 1
Average over 5 runs: 0.002 seconds
For ig_fet: Number of inputs: 10
Average over 5 runs: 0.004 seconds
For ig_fet: Number of inputs: 100
Average over 5 runs: 0.002 seconds
For ig_fet: Number of inputs: 500
Average over 5 runs: 0.002 seconds
For ig_fet: Number of inputs: 1000
Average over 5 runs: 0.003 seconds
For ig_fet: Number of inputs: 5000
Average over 5 runs: 0.005 seconds
For ig_fet: Number of inputs: 10000
Average over 5 runs: 0.008 seconds
For ig_fet: Number of inputs: 25000
Average over 5 runs: 0.016 seconds
For ig_fet: Number of inputs: 50000
Average over 5 runs: 0.031 seconds
For ig_fet: Number of inputs: 75000
Average over 5 runs: 0.045 seconds
For ig_fet: Number of inputs: 100000
Average over 5 runs: 0.060 seconds

. 
. log close

. 
end of do-file

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_00000c.tmp"

. clear all

. 
. local bench_folder "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. log using "`bench_folder'\statabench.log", replace text nomsg

. 
. // WHO Child Growth Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_who_gs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For who_gs gigs_stata - Number of inputs: `i'"
  5.         bench, reps(5) restore last: ///
>                 qui egen double z_gigs = who_gs(y, "wfa", "v2z"), ///
>                         xvar(x) sex(sex) sexcode(m=M, f=F)
  6. }
For who_gs gigs_stata - Number of inputs: 1
Average over 5 runs: 0.021 seconds
For who_gs gigs_stata - Number of inputs: 10
Average over 5 runs: 0.010 seconds
For who_gs gigs_stata - Number of inputs: 100
Average over 5 runs: 0.009 seconds
For who_gs gigs_stata - Number of inputs: 500
Average over 5 runs: 0.015 seconds
For who_gs gigs_stata - Number of inputs: 1000
Average over 5 runs: 0.012 seconds
For who_gs gigs_stata - Number of inputs: 5000
Average over 5 runs: 0.027 seconds
For who_gs gigs_stata - Number of inputs: 10000
Average over 5 runs: 0.040 seconds
For who_gs gigs_stata - Number of inputs: 25000
Average over 5 runs: 0.091 seconds
For who_gs gigs_stata - Number of inputs: 50000
Average over 5 runs: 0.178 seconds
For who_gs gigs_stata - Number of inputs: 75000
Average over 5 runs: 0.266 seconds
For who_gs gigs_stata - Number of inputs: 100000
Average over 5 runs: 0.356 seconds

. 
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_who_gs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For who_gs zanthro - Number of inputs: `i'"
  5.         bench, reps(5) restore last: ///
>                 qui egen z_anthro = zanthro(y, wa, WHO), xvar(x) gender(sex) ///
>                         gencode(male=M, female=F) ageunit(day)
  6. }
For who_gs zanthro - Number of inputs: 1
Average over 5 runs: 0.009 seconds
For who_gs zanthro - Number of inputs: 10
Average over 5 runs: 0.010 seconds
For who_gs zanthro - Number of inputs: 100
Average over 5 runs: 0.011 seconds
For who_gs zanthro - Number of inputs: 500
Average over 5 runs: 0.015 seconds
For who_gs zanthro - Number of inputs: 1000
Average over 5 runs: 0.020 seconds
For who_gs zanthro - Number of inputs: 5000
Average over 5 runs: 0.066 seconds
For who_gs zanthro - Number of inputs: 10000
Average over 5 runs: 0.118 seconds
For who_gs zanthro - Number of inputs: 25000
Average over 5 runs: 0.270 seconds
For who_gs zanthro - Number of inputs: 50000
Average over 5 runs: 0.529 seconds
For who_gs zanthro - Number of inputs: 75000
Average over 5 runs: 0.793 seconds
For who_gs zanthro - Number of inputs: 100000
Average over 5 runs: 1.070 seconds

. 
. // IG-21st Newborn Size Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_nbs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For ig_nbs gigs_stata - Number of inputs: `i'"
  5.         bench, reps(5) restore last: ///
>                 qui egen double z_gigs = ig_nbs(y, "wfga", "v2z"), ///
>                         gest_days(x) sex(sex) sexcode(m=M, f=F)
  6. }
For ig_nbs gigs_stata - Number of inputs: 1
Average over 5 runs: 0.004 seconds
For ig_nbs gigs_stata - Number of inputs: 10
Average over 5 runs: 0.004 seconds
For ig_nbs gigs_stata - Number of inputs: 100
Average over 5 runs: 0.004 seconds
For ig_nbs gigs_stata - Number of inputs: 500
Average over 5 runs: 0.005 seconds
For ig_nbs gigs_stata - Number of inputs: 1000
Average over 5 runs: 0.008 seconds
For ig_nbs gigs_stata - Number of inputs: 5000
Average over 5 runs: 0.022 seconds
For ig_nbs gigs_stata - Number of inputs: 10000
Average over 5 runs: 0.042 seconds
For ig_nbs gigs_stata - Number of inputs: 25000
Average over 5 runs: 0.096 seconds
For ig_nbs gigs_stata - Number of inputs: 50000
Average over 5 runs: 0.185 seconds
For ig_nbs gigs_stata - Number of inputs: 75000
Average over 5 runs: 0.279 seconds
For ig_nbs gigs_stata - Number of inputs: 100000
Average over 5 runs: 0.385 seconds

. 
. // IG-21st Postnatal Growth Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_png.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For ig_png gigs_stata - Number of inputs: `i'"
  5.         bench, reps(5) restore last: ///
>                 qui egen double z_gigs = ig_png(y, "wfa", "v2z"), ///
>                         xvar(x) sex(sex) sexcode(m=M, f=F)
  6. }
For ig_png gigs_stata - Number of inputs: 1
Average over 5 runs: 0.002 seconds
For ig_png gigs_stata - Number of inputs: 10
Average over 5 runs: 0.004 seconds
For ig_png gigs_stata - Number of inputs: 100
Average over 5 runs: 0.002 seconds
For ig_png gigs_stata - Number of inputs: 500
Average over 5 runs: 0.003 seconds
For ig_png gigs_stata - Number of inputs: 1000
Average over 5 runs: 0.004 seconds
For ig_png gigs_stata - Number of inputs: 5000
Average over 5 runs: 0.008 seconds
For ig_png gigs_stata - Number of inputs: 10000
Average over 5 runs: 0.014 seconds
For ig_png gigs_stata - Number of inputs: 25000
Average over 5 runs: 0.032 seconds
For ig_png gigs_stata - Number of inputs: 50000
Average over 5 runs: 0.056 seconds
For ig_png gigs_stata - Number of inputs: 75000
Average over 5 runs: 0.082 seconds
For ig_png gigs_stata - Number of inputs: 100000
Average over 5 runs: 0.110 seconds

. 
. // IG-21st Fetal Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_fet.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For ig_fet gigs_stata - Number of inputs: `i'"
  5.         bench, reps(5) restore last: ///
>                 qui egen double z_gigs = ig_fet(y, "ofdfga", "v2z"), xvar(x)
  6. }
For ig_fet gigs_stata - Number of inputs: 1
Average over 5 runs: 0.002 seconds
For ig_fet gigs_stata - Number of inputs: 10
Average over 5 runs: 0.005 seconds
For ig_fet gigs_stata - Number of inputs: 100
Average over 5 runs: 0.002 seconds
For ig_fet gigs_stata - Number of inputs: 500
Average over 5 runs: 0.003 seconds
For ig_fet gigs_stata - Number of inputs: 1000
Average over 5 runs: 0.003 seconds
For ig_fet gigs_stata - Number of inputs: 5000
Average over 5 runs: 0.006 seconds
For ig_fet gigs_stata - Number of inputs: 10000
Average over 5 runs: 0.010 seconds
For ig_fet gigs_stata - Number of inputs: 25000
Average over 5 runs: 0.018 seconds
For ig_fet gigs_stata - Number of inputs: 50000
Average over 5 runs: 0.029 seconds
For ig_fet gigs_stata - Number of inputs: 75000
Average over 5 runs: 0.044 seconds
For ig_fet gigs_stata - Number of inputs: 100000
Average over 5 runs: 0.062 seconds

. 
. log close

. 
end of do-file

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_00000d.tmp"

. log close
no log file open
r(606);

end of do-file

r(606);

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_00000e.tmp"

. local benchmarking_rscript = "`bench_folder'\statabench2csv.R"

. if "`c(os)'"=="MacOSX" | "`c(os)'"=="UNIX" {
.     noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("/usr/local/bin/R") ///
>                 roptions(`"--vanilla"')
. }

. else if "`c(os)'"=="Windows" { 
.         // Windows
.         // n.b. Set `rversion' to that of own system - n.b. gigs needs R >=4.1.0
.         local rversion "4.3.3" 
.         noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe") ///
>                 roptions("--vanilla")
file \statabench2csv.R not found
r(601);
. }
r(601);

end of do-file

r(601);

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_00000f.tmp"

. local bench_folder "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. 
end of do-file

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_00000g.tmp"

. local benchmarking_rscript "`bench_folder'/statabench2csv.R"

. 
end of do-file

. di "`benchmarking_rscript'"
D:\Users\tadeo\Documents\gigs-stata\benchmarking\statabench2csv.R

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_00000h.tmp"

. if "`c(os)'"=="MacOSX" | "`c(os)'"=="UNIX" {
.     noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("/usr/local/bin/R") ///
>                 roptions(`"--vanilla"')
. }

. else if "`c(os)'"=="Windows" { 
.         // Windows
.         // n.b. Set `rversion' to that of own system - n.b. gigs needs R >=4.1.0
.         local rversion "4.3.3" 
.         noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe") ///
>                 roptions("--vanilla")
invalid file specification
r(198);
. }
r(198);

end of do-file

r(198);

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_00000i.tmp"

.         noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe") ///
>                 roptions("--vanilla")
invalid file specification
r(198);

end of do-file

r(198);

. set trace on

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_00000j.tmp"

. local benchmarking_rscript "`bench_folder'/statabench2csv.R"

. if "`c(os)'"=="MacOSX" | "`c(os)'"=="UNIX" {
.     noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("/usr/local/bin/R") ///
>                 roptions(`"--vanilla"')
. }

. else if "`c(os)'"=="Windows" { 
.         // Windows
.         // n.b. Set `rversion' to that of own system - n.b. gigs needs R >=4.1.0
.         local rversion "4.3.3" 
.         noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe") ///
>                 roptions("--vanilla")
  --------------------------------------------------------------------------------------------------------------------------- begin rsource ---
  - version 10.0
  - syntax [ using/ ] [ , TErminator(string) MAXlines(numlist min=1 max=1 >=0) RPath(string) ROptions(string) noLOutput LSource ]
  - if "`maxlines'"=="" {
  = if ""=="" {
  - local maxlines=1024
  - }
  - if `"`rpath'"'=="" {
  = if `"C:\Program Files\R\R-4.3.3\bin\x64\Rterm.exe"'=="" {
    local rpath `"$Rterm_path"'
    if `"`rpath'"'=="" {
    local rpath = cond(c(os) == "Windows", "Rterm.exe", "/usr/bin/r")
    }
    }
  - if `"`roptions'"'=="" {
  = if `"--vanilla"'=="" {
    local roptions `"$Rterm_options"'
    }
  - tempname sterminator scurline
  - mata: st_strscalar("`sterminator'",strtrim(st_local("terminator")))
  = mata: st_strscalar("__000000",strtrim(st_local("terminator")))
  - if `"`using'"'=="" & `sterminator'=="" {
  = if `"/statabench2csv.R"'=="" & __000000=="" {
    disp as error "You must specify either using <filename> or a terminator() string "
    error 498
    }
  - tempfile tempsource
  - tempname tempbf
  - qui file open `tempbf' using `"`tempsource'"', write text replace
  = qui file open __000002 using `"C:\Users\tadeo\AppData\Local\Temp\ST_fbc_000001.tmp"', write text replace
  - if `sterminator'!="" {
  = if __000000!="" {
    qui disp _request2(_curline)
    mata: st_strscalar("`scurline'",st_local("curline"))
    local nline=0
    while `scurline'!=`sterminator' & `nline'<`maxlines' {
    file write `tempbf' (`scurline') _n
    local nline=`nline'+1
    qui disp _request2(_curline)
    mata: st_strscalar("`scurline'",st_local("curline"))
    }
    }
  - if `"`using'"'!="" {
  = if `"/statabench2csv.R"'!="" {
  - tempname inbuff
  - file open `inbuff' using `"`using'"', read text
  = file open __000003 using `"/statabench2csv.R"', read text
file /statabench2csv.R not found
    file read `inbuff' curline
    while !r(eof) {
    mata: st_strscalar("`scurline'",st_local("curline"))
    file write `tempbf' (`scurline') _n
    file read `inbuff' curline
    }
    file close `inbuff'
    }
  ----------------------------------------------------------------------------------------------------------------------------- end rsource ---
r(601);
. }
r(601);

end of do-file

r(601);

. do "C:\Users\tadeo\AppData\Local\Temp\STDfbc_00000k.tmp"

. local bench_folder "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. local benchmarking_rscript "`bench_folder'/statabench2csv.R"

. if "`c(os)'"=="MacOSX" | "`c(os)'"=="UNIX" {
.     noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("/usr/local/bin/R") ///
>                 roptions(`"--vanilla"')
. }

. else if "`c(os)'"=="Windows" { 
.         // Windows
.         // n.b. Set `rversion' to that of own system - n.b. gigs needs R >=4.1.0
.         local rversion "4.3.3" 
.         noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe") ///
>                 roptions("--vanilla")
  --------------------------------------------------------------------------------------------------------------------------- begin rsource ---
  - version 10.0
  - syntax [ using/ ] [ , TErminator(string) MAXlines(numlist min=1 max=1 >=0) RPath(string) ROptions(string) noLOutput LSource ]
  - if "`maxlines'"=="" {
  = if ""=="" {
  - local maxlines=1024
  - }
  - if `"`rpath'"'=="" {
  = if `"C:\Program Files\R\R-4.3.3\bin\x64\Rterm.exe"'=="" {
    local rpath `"$Rterm_path"'
    if `"`rpath'"'=="" {
    local rpath = cond(c(os) == "Windows", "Rterm.exe", "/usr/bin/r")
    }
    }
  - if `"`roptions'"'=="" {
  = if `"--vanilla"'=="" {
    local roptions `"$Rterm_options"'
    }
  - tempname sterminator scurline
  - mata: st_strscalar("`sterminator'",strtrim(st_local("terminator")))
  = mata: st_strscalar("__000000",strtrim(st_local("terminator")))
  - if `"`using'"'=="" & `sterminator'=="" {
  = if `"D:\Users\tadeo\Documents\gigs-stata\benchmarking/statabench2csv.R"'=="" & __000000=="" {
    disp as error "You must specify either using <filename> or a terminator() string "
    error 498
    }
  - tempfile tempsource
  - tempname tempbf
  - qui file open `tempbf' using `"`tempsource'"', write text replace
  = qui file open __000002 using `"C:\Users\tadeo\AppData\Local\Temp\ST_fbc_000001.tmp"', write text replace
  - if `sterminator'!="" {
  = if __000000!="" {
    qui disp _request2(_curline)
    mata: st_strscalar("`scurline'",st_local("curline"))
    local nline=0
    while `scurline'!=`sterminator' & `nline'<`maxlines' {
    file write `tempbf' (`scurline') _n
    local nline=`nline'+1
    qui disp _request2(_curline)
    mata: st_strscalar("`scurline'",st_local("curline"))
    }
    }
  - if `"`using'"'!="" {
  = if `"D:\Users\tadeo\Documents\gigs-stata\benchmarking/statabench2csv.R"'!="" {
  - tempname inbuff
  - file open `inbuff' using `"`using'"', read text
  = file open __000003 using `"D:\Users\tadeo\Documents\gigs-stata\benchmarking/statabench2csv.R"', read text
  - file read `inbuff' curline
  = file read __000003 curline
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
  - mata: st_strscalar("`scurline'",st_local("curline"))
  = mata: st_strscalar("__000001",st_local("curline"))
  - file write `tempbf' (`scurline') _n
  = file write __000002 (__000001) _n
  - file read `inbuff' curline
  = file read __000003 curline
  - }
  - while !r(eof) {
    mata: st_strscalar("`scurline'",st_local("curline"))
    file write `tempbf' (`scurline') _n
    file read `inbuff' curline
    }
  - file close `inbuff'
  = file close __000003
  - }
  - qui file close `tempbf'
  = qui file close __000002
  - disp as text "Assumed R program path: " as result `""`rpath'""'
  = disp as text "Assumed R program path: " as result `""C:\Program Files\R\R-4.3.3\bin\x64\Rterm.exe""'
Assumed R program path: "C:\Program Files\R\R-4.3.3\bin\x64\Rterm.exe"
  - if "`lsource'"=="lsource" {
  = if ""=="lsource" {
    disp as text "Beginning of listing of R source code "
    type `"`tempsource'"'
    disp as text "End of listing of R source code "
    }
  - tempfile templis
  - local Rcommand `""`rpath'" `roptions' < "`tempsource'" > "`templis'""'
  = local Rcommand `""C:\Program Files\R\R-4.3.3\bin\x64\Rterm.exe" --vanilla < "C:\Users\tadeo\AppData\Local\Temp\ST_fbc_000001.tmp" > "C:\Use
> rs\tadeo\AppData\Local\Temp\ST_fbc_000002.tmp""'
  - shell `Rcommand'
  = shell "C:\Program Files\R\R-4.3.3\bin\x64\Rterm.exe" --vanilla < "C:\Users\tadeo\AppData\Local\Temp\ST_fbc_000001.tmp" > "C:\Users\tadeo\Ap
> pData\Local\Temp\ST_fbc_000002.tmp"
  - if "`loutput'"!="noloutput" {
  = if "noloutput"!="noloutput" {
    disp as text "Beginning of R output"
    type `"`templis'"'
    disp as text "End of R output"
    }
  ----------------------------------------------------------------------------------------------------------------------------- end rsource ---
. }

. 
end of do-file

. set trace off

. do "D:\Users\tadeo\Documents\gigs-stata\benchmarking\statabench.do"

. /* 
>   The benchmarking code here is used to compare and contrast different versions
>   of the Stata code with each other, and with R implementations. 
> 
>   The benchmarking tables used are the same used to benchmark the various R/SAS 
>   implementations of these growth standards against each other. The generation
>   process for these tables can be checked out on the R package website in the 
>   benchmarking article 
>   (https://docs.ropensci.org/gigs/articles/benchmarking.html), and you can go to
>   GitHub and run the source code yourself.
>   
>   It is assumed that a user running this script has 'gigs' and 'zanthro' for
>   installed in Stata, as stable releases or for 'gigs', the dev version from 
>   GitHub.
> */
. 
. clear all

. 
. local bench_folder "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. log using "`bench_folder'\statabench.log", replace text nomsg

. 
. // WHO Child Growth Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_who_gs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For who_gs gigs_stata - Number of inputs: `i'"
  5.         bench, reps(50) restore last: ///
>                 qui egen double z_gigs = who_gs(y, "wfa", "v2z"), ///
>                         xvar(x) sex(sex) sexcode(m=M, f=F)
  6. }
For who_gs gigs_stata - Number of inputs: 1
Average over 50 runs: 0.008 seconds
For who_gs gigs_stata - Number of inputs: 10
Average over 50 runs: 0.009 seconds
For who_gs gigs_stata - Number of inputs: 100
Average over 50 runs: 0.009 seconds
For who_gs gigs_stata - Number of inputs: 500
Average over 50 runs: 0.010 seconds
For who_gs gigs_stata - Number of inputs: 1000
Average over 50 runs: 0.012 seconds
For who_gs gigs_stata - Number of inputs: 5000
Average over 50 runs: 0.024 seconds
For who_gs gigs_stata - Number of inputs: 10000
Average over 50 runs: 0.040 seconds
For who_gs gigs_stata - Number of inputs: 25000
Average over 50 runs: 0.087 seconds
For who_gs gigs_stata - Number of inputs: 50000
Average over 50 runs: 0.169 seconds
For who_gs gigs_stata - Number of inputs: 75000
Average over 50 runs: 0.256 seconds
For who_gs gigs_stata - Number of inputs: 100000
Average over 50 runs: 0.351 seconds

. 
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_who_gs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For who_gs zanthro - Number of inputs: `i'"
  5.         bench, reps(50) restore last: ///
>                 qui egen z_anthro = zanthro(y, wa, WHO), xvar(x) gender(sex) ///
>                         gencode(male=M, female=F) ageunit(day)
  6. }
For who_gs zanthro - Number of inputs: 1
Average over 50 runs: 0.007 seconds
For who_gs zanthro - Number of inputs: 10
Average over 50 runs: 0.008 seconds
For who_gs zanthro - Number of inputs: 100
Average over 50 runs: 0.008 seconds
For who_gs zanthro - Number of inputs: 500
Average over 50 runs: 0.013 seconds
For who_gs zanthro - Number of inputs: 1000
Average over 50 runs: 0.018 seconds
For who_gs zanthro - Number of inputs: 5000
Average over 50 runs: 0.060 seconds
For who_gs zanthro - Number of inputs: 10000
Average over 50 runs: 0.111 seconds
For who_gs zanthro - Number of inputs: 25000
Average over 50 runs: 0.266 seconds
For who_gs zanthro - Number of inputs: 50000
Average over 50 runs: 0.525 seconds
For who_gs zanthro - Number of inputs: 75000
Average over 50 runs: 0.789 seconds
For who_gs zanthro - Number of inputs: 100000
Average over 50 runs: 1.063 seconds

. 
. // IG-21st Newborn Size Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_nbs.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For ig_nbs gigs_stata - Number of inputs: `i'"
  5.         bench, reps(50) restore last: ///
>                 qui egen double z_gigs = ig_nbs(y, "wfga", "v2z"), ///
>                         gest_days(x) sex(sex) sexcode(m=M, f=F)
  6. }
For ig_nbs gigs_stata - Number of inputs: 1
Average over 50 runs: 0.004 seconds
For ig_nbs gigs_stata - Number of inputs: 10
Average over 50 runs: 0.004 seconds
For ig_nbs gigs_stata - Number of inputs: 100
Average over 50 runs: 0.004 seconds
For ig_nbs gigs_stata - Number of inputs: 500
Average over 50 runs: 0.006 seconds
For ig_nbs gigs_stata - Number of inputs: 1000
Average over 50 runs: 0.008 seconds
For ig_nbs gigs_stata - Number of inputs: 5000
Average over 50 runs: 0.022 seconds
For ig_nbs gigs_stata - Number of inputs: 10000
Average over 50 runs: 0.040 seconds
For ig_nbs gigs_stata - Number of inputs: 25000
Average over 50 runs: 0.093 seconds
For ig_nbs gigs_stata - Number of inputs: 50000
Average over 50 runs: 0.183 seconds
For ig_nbs gigs_stata - Number of inputs: 75000
Average over 50 runs: 0.277 seconds
For ig_nbs gigs_stata - Number of inputs: 100000
Average over 50 runs: 0.382 seconds

. 
. // IG-21st Postnatal Growth Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_png.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For ig_png gigs_stata - Number of inputs: `i'"
  5.         bench, reps(50) restore last: ///
>                 qui egen double z_gigs = ig_png(y, "wfa", "v2z"), ///
>                         xvar(x) sex(sex) sexcode(m=M, f=F)
  6. }
For ig_png gigs_stata - Number of inputs: 1
Average over 50 runs: 0.002 seconds
For ig_png gigs_stata - Number of inputs: 10
Average over 50 runs: 0.002 seconds
For ig_png gigs_stata - Number of inputs: 100
Average over 50 runs: 0.002 seconds
For ig_png gigs_stata - Number of inputs: 500
Average over 50 runs: 0.003 seconds
For ig_png gigs_stata - Number of inputs: 1000
Average over 50 runs: 0.003 seconds
For ig_png gigs_stata - Number of inputs: 5000
Average over 50 runs: 0.008 seconds
For ig_png gigs_stata - Number of inputs: 10000
Average over 50 runs: 0.014 seconds
For ig_png gigs_stata - Number of inputs: 25000
Average over 50 runs: 0.030 seconds
For ig_png gigs_stata - Number of inputs: 50000
Average over 50 runs: 0.054 seconds
For ig_png gigs_stata - Number of inputs: 75000
Average over 50 runs: 0.080 seconds
For ig_png gigs_stata - Number of inputs: 100000
Average over 50 runs: 0.108 seconds

. 
. // IG-21st Fetal Standards
. foreach i in 1 10 100 500 1000 5000 10000 25000 50000 75000 100000 {
  2.         use "`bench_folder'/bench_ds_ig_fet.dta", clear
  3.         qui drop if _n > `i'
  4.         di "For ig_fet gigs_stata - Number of inputs: `i'"
  5.         bench, reps(50) restore last: ///
>                 qui egen double z_gigs = ig_fet(y, "ofdfga", "v2z"), xvar(x)
  6. }
For ig_fet gigs_stata - Number of inputs: 1
Average over 50 runs: 0.002 seconds
For ig_fet gigs_stata - Number of inputs: 10
Average over 50 runs: 0.002 seconds
For ig_fet gigs_stata - Number of inputs: 100
Average over 50 runs: 0.002 seconds
For ig_fet gigs_stata - Number of inputs: 500
Average over 50 runs: 0.002 seconds
For ig_fet gigs_stata - Number of inputs: 1000
Average over 50 runs: 0.003 seconds
For ig_fet gigs_stata - Number of inputs: 5000
Average over 50 runs: 0.005 seconds
For ig_fet gigs_stata - Number of inputs: 10000
Average over 50 runs: 0.008 seconds
For ig_fet gigs_stata - Number of inputs: 25000
Average over 50 runs: 0.016 seconds
For ig_fet gigs_stata - Number of inputs: 50000
Average over 50 runs: 0.030 seconds
For ig_fet gigs_stata - Number of inputs: 75000
Average over 50 runs: 0.042 seconds
For ig_fet gigs_stata - Number of inputs: 100000
Average over 50 runs: 0.058 seconds

. 
. log close

. 
. // Convert benchmarking log to a CSV file used in R benchmarking article
. // n.b. Install rsource (R from Stata) if not already installed:
. //              . cap ssc install rsource, replace
. local bench_folder "D:\Users\tadeo\Documents\gigs-stata\benchmarking"

. local benchmarking_rscript "`bench_folder'/statabench2csv.R"

. if "`c(os)'"=="MacOSX" | "`c(os)'"=="UNIX" {
.     noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("/usr/local/bin/R") ///
>                 roptions(`"--vanilla"')
. }

. else if "`c(os)'"=="Windows" { 
.         // Windows
.         // n.b. Set `rversion' to that of own system - n.b. gigs needs R >=4.1.0
.         local rversion "4.3.3" 
.         noi rsource using "`benchmarking_rscript'", ///
>                 noloutput ///
>                 rpath("C:\Program Files\R\R-`rversion'\bin\x64\Rterm.exe") ///
>                 roptions("--vanilla")
Assumed R program path: "C:\Program Files\R\R-4.3.3\bin\x64\Rterm.exe"
. }

. 
. // OLD VERSIONS OF GIGS --------------------------------------------------------
. // Kept to note how development is progressing
. 
. // All versions before 1.0.0 just benched on the WHO GS weight-for-age standard
. 
. // gigs 0.4.0
. // Number of inputs: 1
. // Average over 25 runs: 0.009 seconds
. // Number of inputs: 10
. // Average over 25 runs: 0.008 seconds
. // Number of inputs: 100
. // Average over 25 runs: 0.009 seconds
. // Number of inputs: 500
. // Average over 25 runs: 0.010 seconds
. // Number of inputs: 1000
. // Average over 25 runs: 0.012 seconds
. // Number of inputs: 5000
. // Average over 25 runs: 0.028 seconds
. // Number of inputs: 10000
. // Average over 25 runs: 0.047 seconds
. // Number of inputs: 25000
. // Average over 25 runs: 0.104 seconds
. // Number of inputs: 50000
. // Average over 25 runs: 0.198 seconds
. // Number of inputs: 75000
. // Average over 25 runs: 0.296 seconds
. // Number of inputs: 100000
. // Average over 25 runs: 0.405 seconds
. 
. // gigs 0.3.2:
. // Number of inputs: 1
. // Average over 25 runs: 0.008 seconds
. // Number of inputs: 10
. // Average over 25 runs: 0.009 seconds
. // Number of inputs: 100
. // Average over 25 runs: 0.009 seconds
. // Number of inputs: 500
. // Average over 25 runs: 0.010 seconds
. // Number of inputs: 1000
. // Average over 25 runs: 0.012 seconds
. // Number of inputs: 5000
. // Average over 25 runs: 0.027 seconds
. // Number of inputs: 10000
. // Average over 25 runs: 0.045 seconds
. // Number of inputs: 25000
. // Average over 25 runs: 0.101 seconds
. // Number of inputs: 50000
. // Average over 25 runs: 0.195 seconds
. // Number of inputs: 75000
. // Average over 25 runs: 0.294 seconds
. // Number of inputs: 100000
. // Average over 25 runs: 0.405 seconds
. 
. // gigs 0.3.1:
. // Number of inputs: 1
. // Average over 25 runs: 0.008 seconds
. // Number of inputs: 10
. // Average over 25 runs: 0.009 seconds
. // Number of inputs: 100
. // Average over 25 runs: 0.009 seconds
. // Number of inputs: 500
. // Average over 25 runs: 0.010 seconds
. // Number of inputs: 1000
. // Average over 25 runs: 0.012 seconds
. // Number of inputs: 5000
. // Average over 25 runs: 0.028 seconds
. // Number of inputs: 10000
. // Average over 25 runs: 0.047 seconds
. // Number of inputs: 25000
. // Average over 25 runs: 0.106 seconds
. // Number of inputs: 50000
. // Average over 25 runs: 0.204 seconds
. // Number of inputs: 75000
. // Average over 25 runs: 0.310 seconds
. // Number of inputs: 100000
. // Average over 25 runs: 0.410 seconds
. 
. // gigs 0.3.0:
. // Number of inputs: 1
. // Average over 25 runs: 0.008 seconds
. // Number of inputs: 10
. // Average over 25 runs: 0.010 seconds
. // Number of inputs: 100
. // Average over 25 runs: 0.010 seconds
. // Number of inputs: 500
. // Average over 25 runs: 0.011 seconds
. // Number of inputs: 1000
. // Average over 25 runs: 0.013 seconds
. // Number of inputs: 5000
. // Average over 25 runs: 0.028 seconds
. // Number of inputs: 10000
. // Average over 25 runs: 0.048 seconds
. // Number of inputs: 25000
. // Average over 25 runs: 0.107 seconds
. // Number of inputs: 50000
. // Average over 25 runs: 0.205 seconds
. // Number of inputs: 75000
. // Average over 25 runs: 0.313 seconds
. // Number of inputs: 100000
. // Average over 25 runs: 0.431 seconds
. 
. // gigs 0.2.4:
. // Number of inputs: 1
. // Average over 25 runs: 0.016 seconds
. // Number of inputs: 10
. // Average over 25 runs: 0.015 seconds
. // Number of inputs: 100
. // Average over 25 runs: 0.017 seconds
. // Number of inputs: 500
. // Average over 25 runs: 0.039 seconds
. // Number of inputs: 1000
. // Average over 25 runs: 0.079 seconds
. // Number of inputs: 5000
. // Average over 25 runs: 1.007 seconds
. // --> Crashed on 10000 or more inputs
. 
end of do-file

. exit, clear