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scratch that--just show CV
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@kkranker
kkranker committed Oct 22, 2019
1 parent 21dbb26 commit 2a819e1
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Binary file modified sims/sim11/1-row summary.dta
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28 changes: 14 additions & 14 deletions sims/sim11/LaTeX summary.tex
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Expand Up @@ -56,19 +56,19 @@

columns refer to N and 0/1 for regression adjustment.
\begin{center}
\begin{tabular}{rrrrr}
\hline \noalign{\smallskip} & wgt_cv50 & wgt_skewness50 & bal_mean_asd50 & bal_max_asd50\\
\noalign{\smallskip}\hline \noalign{\smallskip}Logit regression & 0.678 & 0.416 & 0.147 & 0.270\\
Logit regression, with trimming & 0.683 & 0.419 & 0.148 & 0.270\\
Random forest classifier & 0.348 & 0.217 & 0.274 & 0.467\\
CBPS, over identified & 0.524 & 0.338 & 0.152 & 0.284\\
CBPS, just identified & 0.728 & 0.373 & 0.034 & 0.077\\
PCBPS 99% & 0.727 & 0.373 & 0.032 & 0.072\\
PCBPS 97% & 0.718 & 0.372 & 0.035 & 0.078\\
PCBPS 95% & 0.706 & 0.369 & 0.040 & 0.088\\
PCBPS 90% & 0.675 & 0.361 & 0.053 & 0.115\\
PCBPS 75% & 0.572 & 0.333 & 0.099 & 0.213\\
True propensity score & 0.574 & 0.353 & 0.289 & 0.512\\
None (unweighted data) & 0.000 & & 0.414 & 0.783\\
\begin{tabular}{rrrr}
\hline \noalign{\smallskip} & wgt_cv50 & bal_mean_asd50 & bal_max_asd50\\
\noalign{\smallskip}\hline \noalign{\smallskip}Logit regression & 0.678 & 0.147 & 0.270\\
Logit regression, with trimming & 0.683 & 0.148 & 0.270\\
Random forest classifier & 0.348 & 0.274 & 0.467\\
CBPS, over identified & 0.524 & 0.152 & 0.284\\
CBPS, just identified & 0.728 & 0.034 & 0.077\\
PCBPS 99% & 0.727 & 0.032 & 0.072\\
PCBPS 97% & 0.718 & 0.035 & 0.078\\
PCBPS 95% & 0.706 & 0.040 & 0.088\\
PCBPS 90% & 0.675 & 0.053 & 0.115\\
PCBPS 75% & 0.572 & 0.099 & 0.213\\
True propensity score & 0.574 & 0.289 & 0.512\\
None (unweighted data) & 0.000 & 0.414 & 0.783\\
\noalign{\smallskip}\hline\end{tabular}\\
\end{center}
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85 changes: 42 additions & 43 deletions sims/sim11/summary_tables.log
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Expand Up @@ -2,7 +2,7 @@
name: sim11_tables
log: C:\Users\kkranker\Documents\Stata\psweight\code-psweight\sims/sim11/summary_tables.log
log type: text
opened on: 21 Oct 2019, 20:42:43
opened on: 21 Oct 2019, 20:56:15

.
. *! BPO CBPS Simulation
Expand Down Expand Up @@ -1151,14 +1151,14 @@ columns refer to N and 0/1 for regression adjustment.
. keep if `ifstmnt' & `2ns'
(180 observations deleted)

. keep estimator N augmented wgt_cv wgt_skewness bal_mean_asd bal_max_asd
. keep estimator N augmented wgt_cv bal_mean_asd bal_max_asd

. keep if aug==0 // these stats are all identical for aug and non-aug estimators
(12 observations deleted)

. local c=0

. foreach v of var wgt_cv wgt_skewness bal_mean_asd bal_max_asd {
. foreach v of var wgt_cv bal_mean_asd bal_max_asd {
2. rename `v' stat`++c'
3. local def `macval(def)' `c' "`v'"
4. }
Expand All @@ -1171,25 +1171,25 @@ columns refer to N and 0/1 for regression adjustment.

. tabdisp estimator N _j, by(augmented) c(stat) cellwidth(6) csep(2) stubwidth(13)

----------------------------------------------------
augmented and | _j and N
RECODE of | - wg - - wg - - ba - - ba -
estimator | 50 50 50 50
--------------+-------------------------------------
------------------------------------------
augmented and | _j and N
RECODE of | - wg - - ba - - ba -
estimator | 50 50 50
--------------+---------------------------
IPW |
Logit regress | 0.678 0.416 0.147 0.270
Logit regress | 0.683 0.419 0.148 0.270
Random forest | 0.348 0.217 0.274 0.467
CBPS, over id | 0.524 0.338 0.152 0.284
CBPS, just id | 0.728 0.373 0.034 0.077
PCBPS 99% | 0.727 0.373 0.032 0.072
PCBPS 97% | 0.718 0.372 0.035 0.078
PCBPS 95% | 0.706 0.369 0.040 0.088
PCBPS 90% | 0.675 0.361 0.053 0.115
PCBPS 75% | 0.572 0.333 0.099 0.213
True propensi | 0.574 0.353 0.289 0.512
None (unweigh | 0.000 0.414 0.783
----------------------------------------------------
Logit regress | 0.678 0.147 0.270
Logit regress | 0.683 0.148 0.270
Random forest | 0.348 0.274 0.467
CBPS, over id | 0.524 0.152 0.284
CBPS, just id | 0.728 0.034 0.077
PCBPS 99% | 0.727 0.032 0.072
PCBPS 97% | 0.718 0.035 0.078
PCBPS 95% | 0.706 0.040 0.088
PCBPS 90% | 0.675 0.053 0.115
PCBPS 75% | 0.572 0.099 0.213
True propensi | 0.574 0.289 0.512
None (unweigh | 0.000 0.414 0.783
------------------------------------------

.
. // rebuild and export to LaTeX snippet
Expand All @@ -1198,27 +1198,26 @@ None (unweigh | 0.000 0.414 0.783
. keep if `ifstmnt' & `2ns'
(180 observations deleted)

. keep estimator N augmented wgt_cv wgt_skewness bal_mean_asd bal_max_asd
. keep estimator N augmented wgt_cv bal_mean_asd bal_max_asd

. keep if aug==0 // these stats are all identical for aug and non-aug estimators
(12 observations deleted)

. reshape wide wgt_cv wgt_skewness bal_mean_asd bal_max_asd , i(estimator) j(N)
. reshape wide wgt_cv bal_mean_asd bal_max_asd , i(estimator) j(N)
(note: j = 50)

Data long -> wide
-----------------------------------------------------------------------------
Number of obs. 12 -> 12
Number of variables 7 -> 6
Number of variables 6 -> 5
j variable (1 values) N -> (dropped)
xij variables:
wgt_cv -> wgt_cv50
wgt_skewness -> wgt_skewness50
bal_mean_asd -> bal_mean_asd50
bal_max_asd -> bal_max_asd50
-----------------------------------------------------------------------------

. unab vvv: wgt_cv* wgt_skewness* bal_mean_asd* bal_max_asd*
. unab vvv: wgt_cv* bal_mean_asd* bal_max_asd*

. mata: st_matrix("dta", st_data(., "`vvv'"))

Expand All @@ -1228,22 +1227,22 @@ xij variables:

. frmttable using "sims/sim`sim'/LaTeX summary.tex", s(dta) tex addtable fragment sdec(3) coljust(r{r}r)

--------------------------------------------------------------------------------------------
wgt_cv50 wgt_skewness50 bal_mean_asd50 bal_max_asd50
--------------------------------------------------------------------------------------------
Logit regression 0.678 0.416 0.147 0.270
Logit regression, with trimming 0.683 0.419 0.148 0.270
Random forest classifier 0.348 0.217 0.274 0.467
CBPS, over identified 0.524 0.338 0.152 0.284
CBPS, just identified 0.728 0.373 0.034 0.077
PCBPS 99% 0.727 0.373 0.032 0.072
PCBPS 97% 0.718 0.372 0.035 0.078
PCBPS 95% 0.706 0.369 0.040 0.088
PCBPS 90% 0.675 0.361 0.053 0.115
PCBPS 75% 0.572 0.333 0.099 0.213
True propensity score 0.574 0.353 0.289 0.512
None (unweighted data) 0.000 0.414 0.783
--------------------------------------------------------------------------------------------
----------------------------------------------------------------------------
wgt_cv50 bal_mean_asd50 bal_max_asd50
----------------------------------------------------------------------------
Logit regression 0.678 0.147 0.270
Logit regression, with trimming 0.683 0.148 0.270
Random forest classifier 0.348 0.274 0.467
CBPS, over identified 0.524 0.152 0.284
CBPS, just identified 0.728 0.034 0.077
PCBPS 99% 0.727 0.032 0.072
PCBPS 97% 0.718 0.035 0.078
PCBPS 95% 0.706 0.040 0.088
PCBPS 90% 0.675 0.053 0.115
PCBPS 75% 0.572 0.099 0.213
True propensity score 0.574 0.289 0.512
None (unweighted data) 0.000 0.414 0.783
----------------------------------------------------------------------------


.
Expand All @@ -1254,5 +1253,5 @@ xij variables:
name: sim11_tables
log: C:\Users\kkranker\Documents\Stata\psweight\code-psweight\sims/sim11/summary_tables.log
log type: text
closed on: 21 Oct 2019, 20:43:26
closed on: 21 Oct 2019, 20:56:54
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
10 changes: 5 additions & 5 deletions sims/summary_table.do
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Expand Up @@ -181,10 +181,10 @@ foreach v of var rmse bias impact_est_var {
// table with columns for each statistic
preserve
keep if `ifstmnt' & `2ns'
keep estimator N augmented wgt_cv wgt_skewness bal_mean_asd bal_max_asd
keep estimator N augmented wgt_cv bal_mean_asd bal_max_asd
keep if aug==0 // these stats are all identical for aug and non-aug estimators
local c=0
foreach v of var wgt_cv wgt_skewness bal_mean_asd bal_max_asd {
foreach v of var wgt_cv bal_mean_asd bal_max_asd {
rename `v' stat`++c'
local def `macval(def)' `c' "`v'"
}
Expand All @@ -196,10 +196,10 @@ preserve
// rebuild and export to LaTeX snippet
restore, preserve
keep if `ifstmnt' & `2ns'
keep estimator N augmented wgt_cv wgt_skewness bal_mean_asd bal_max_asd
keep estimator N augmented wgt_cv bal_mean_asd bal_max_asd
keep if aug==0 // these stats are all identical for aug and non-aug estimators
reshape wide wgt_cv wgt_skewness bal_mean_asd bal_max_asd , i(estimator) j(N)
unab vvv: wgt_cv* wgt_skewness* bal_mean_asd* bal_max_asd*
reshape wide wgt_cv bal_mean_asd bal_max_asd , i(estimator) j(N)
unab vvv: wgt_cv* bal_mean_asd* bal_max_asd*
mata: st_matrix("dta", st_data(., "`vvv'"))
mata: st_matrixcolstripe("dta", (J(`:list sizeof vvv', 1, ""), tokens("`vvv'")'))
mata: st_matrixrowstripe("dta", (J(`c(N)', 1, ""), st_vlmap("estimator_recode", st_data(., "estimator"))))
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