library(rgenoud)## ## rgenoud (Version 5.8-3.0, Build Date: 2019-01-22)
## ## See http://sekhon.berkeley.edu/rgenoud for additional documentation.
## ## Please cite software as:
## ## Walter Mebane, Jr. and Jasjeet S. Sekhon. 2011.
## ## ``Genetic Optimization Using Derivatives: The rgenoud package for R.''
## ## Journal of Statistical Software, 42(11): 1-26.
## ##library(Matching)## Loading required package: MASS## ##
## ## Matching (Version 4.9-11, Build Date: 2021-10-18)
## ## See http://sekhon.berkeley.edu/matching for additional documentation.
## ## Please cite software as:
## ## Jasjeet S. Sekhon. 2011. ``Multivariate and Propensity Score Matching
## ## Software with Automated Balance Optimization: The Matching package for R.''
## ## Journal of Statistical Software, 42(7): 1-52.
## ##foo <- read.csv(url("https://course-resources.minerva.kgi.edu/uploaded_files/mke/00089202-1711/daughters.csv"))
head(foo)## X year congress party district statenam name ngirls nboys
## 1 1 1997-1998 105 0 1 HAWAII ABERCROMBIE, NEIL 0 0
## 2 2 1997-1998 105 0 5 NEW YOR ACKERMAN, GARY L. 1 2
## 3 3 1997-1998 105 1 4 ALABAMA ADERHOLT, ROBERT B. 0 0
## 4 4 1997-1998 105 0 1 MAINE ALLEN, THOMAS H. 2 0
## 5 5 1997-1998 105 0 1 NEW JER ANDREWS, ROBERT E. 2 0
## 6 6 1997-1998 105 1 7 TEXAS ARCHER, W.R. 3 4
## totchi anygirls propgirls rgroup statabb statalph region repub srvlng female
## 1 0 0 0.0000000 0 HI 12 9 0 7 0
## 2 3 1 0.3333333 4 NY 33 2 0 15 0
## 3 0 0 0.0000000 1 AL 1 6 1 1 0
## 4 2 1 1.0000000 1 ME 20 1 0 1 0
## 5 2 1 1.0000000 1 NJ 31 2 0 7 0
## 6 7 1 0.4285714 2 TX 44 7 1 27 0
## white bday age demvote medinc perf perw perhs percol
## 1 1 1938-06-26 58 0.57 46389 0.5000151 0.2909628 0.814 0.266
## 2 1 1942-11-19 54 0.60 57915 0.5283446 0.8401480 0.839 0.348
## 3 1 1965-07-22 31 0.43 25401 0.5291693 0.9251785 0.577 0.081
## 4 1 1945-04-16 51 0.52 36067 0.5252141 0.9851513 0.817 0.224
## 5 1 1957-08-04 39 0.59 40674 0.5291277 0.7835612 0.742 0.169
## 6 1 1928-03-22 68 0.28 51258 0.5126253 0.8334154 0.901 0.407
## perur alabort moreserv moredef morecrimesp protgay dr1per dr2per
## 1 0.9979499 NA NA NA NA NA NA NA
## 2 0.9934435 0.5688074 0.4726477 0.1709234 0.5885510 0.7051282 0.30 0.38
## 3 0.3419504 0.2970711 0.5000000 0.3140097 0.5483871 0.5343512 0.69 0.13
## 4 0.4894656 NA NA NA NA NA 0.58 0.24
## 5 0.9607588 0.5555556 0.3771930 0.1647510 0.5729927 0.7071428 0.31 0.37
## 6 0.9580938 0.3655172 0.3251748 0.2549923 0.5853333 0.5652174 0.52 0.28
## dr3per dr4per dr5per aauw rtl nowtot now1 now2 now3 now4 now5 now6 now7 now8
## 1 NA NA NA 100 10 90 1 1 1 1 1 1 1 1
## 2 0.00 0.12 0.19 88 5 90 1 1 1 1 1 1 1 1
## 3 0.02 0.04 0.12 0 100 0 0 0 0 0 0 0 0 0
## 4 0.02 0.03 0.20 100 5 95 1 1 1 1 1 1 1 1
## 5 0.02 0.09 0.20 100 6 90 1 1 1 1 1 1 1 1
## 6 0.01 0.03 0.15 0 100 0 0 0 0 0 0 0 0 0
## now9 now10 now11 now12 now13 now14 now15 now16 now17 now18 now19 now20 none
## 1 1 1 0 0 1 1 1 1 1 1 1 1 1
## 2 1 1 1 0 1 1 0 1 1 1 1 1 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 1 1 1 0 1 1 1 1 1 1 1 1 0
## 5 1 1 1 0 1 0 1 1 1 1 1 1 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## Protestant Catholic OtherC OtherR Christian hasgirls hasboys haskids reg1
## 1 0 0 0 0 1 0 0 0 0
## 2 0 0 0 1 0 1 1 1 0
## 3 1 0 0 0 1 0 0 0 0
## 4 1 0 0 0 1 1 0 1 1
## 5 1 0 0 0 1 1 0 1 0
## 6 0 1 0 0 1 1 1 1 0
## reg2 reg3 reg4 reg5 reg6 reg7 reg8 reg9 Dems Repubs OthParty
## 1 0 0 0 0 0 0 0 1 1 0 0
## 2 1 0 0 0 0 0 0 0 1 0 0
## 3 0 0 0 0 1 0 0 0 0 1 0
## 4 0 0 0 0 0 0 0 0 1 0 0
## 5 1 0 0 0 0 0 0 0 1 0 0
## 6 0 0 0 0 0 1 0 0 0 1 0model <- lm(nowtot ~ Dems + Repubs + Christian + age + srvlng + demvote + hasgirls, data = foo)
summary(model)##
## Call:
## lm(formula = nowtot ~ Dems + Repubs + Christian + age + srvlng +
## demvote + hasgirls, data = foo)
##
## Residuals:
## Min 1Q Median 3Q Max
## -56.028 -10.322 -1.517 11.208 69.642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.6991 18.6306 2.077 0.038390 *
## Dems -8.1022 17.5861 -0.461 0.645238
## Repubs -55.1069 17.6340 -3.125 0.001901 **
## Christian -13.3961 3.7218 -3.599 0.000357 ***
## age 0.1260 0.1117 1.128 0.259938
## srvlng -0.2251 0.1355 -1.662 0.097349 .
## demvote 87.5501 8.4847 10.319 < 2e-16 ***
## hasgirls -0.4523 1.9036 -0.238 0.812322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.19 on 422 degrees of freedom
## Multiple R-squared: 0.7821, Adjusted R-squared: 0.7784
## F-statistic: 216.3 on 7 and 422 DF, p-value: < 2.2e-16confint(model)## 2.5 % 97.5 %
## (Intercept) 2.07870870 75.31946777
## Dems -42.66951687 26.46501755
## Repubs -89.76834880 -20.44543598
## Christian -20.71160536 -6.08052567
## age -0.09351808 0.34542799
## srvlng -0.49145173 0.04119929
## demvote 70.87248062 104.22768129
## hasgirls -4.19406023 3.28952473model_new <- lm(nowtot ~ Repubs + Christian + demvote + hasgirls, data = foo)
summary(model_new)##
## Call:
## lm(formula = nowtot ~ Repubs + Christian + demvote + hasgirls,
## data = foo)
##
## Residuals:
## Min 1Q Median 3Q Max
## -55.584 -10.383 -1.666 11.515 70.581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34.63047 6.40080 5.410 1.05e-07 ***
## Repubs -46.90998 2.13207 -22.002 < 2e-16 ***
## Christian -13.20079 3.63140 -3.635 0.000312 ***
## demvote 87.47064 8.47509 10.321 < 2e-16 ***
## hasgirls 0.04111 1.86040 0.022 0.982382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.19 on 425 degrees of freedom
## Multiple R-squared: 0.7805, Adjusted R-squared: 0.7784
## F-statistic: 377.7 on 4 and 425 DF, p-value: < 2.2e-16confint(model_new)## 2.5 % 97.5 %
## (Intercept) 22.049313 47.211626
## Repubs -51.100690 -42.719274
## Christian -20.338529 -6.063044
## demvote 70.812324 104.128948
## hasgirls -3.615627 3.697841# Check balance of the unmatched set
mb <- MatchBalance(hasgirls ~ Dems + Repubs + Christian + age + srvlng + demvote, data = foo)##
## ***** (V1) Dems *****
## before matching:
## mean treatment........ 0.45833
## mean control.......... 0.50847
## std mean diff......... -10.047
##
## mean raw eQQ diff..... 0.050847
## med raw eQQ diff..... 0
## max raw eQQ diff..... 1
##
## mean eCDF diff........ 0.025071
## med eCDF diff........ 0.025071
## max eCDF diff........ 0.050141
##
## var ratio (Tr/Co)..... 0.98809
## T-test p-value........ 0.35571
##
##
## ***** (V2) Repubs *****
## before matching:
## mean treatment........ 0.53846
## mean control.......... 0.49153
## std mean diff......... 9.4
##
## mean raw eQQ diff..... 0.042373
## med raw eQQ diff..... 0
## max raw eQQ diff..... 1
##
## mean eCDF diff........ 0.023468
## med eCDF diff........ 0.023468
## max eCDF diff........ 0.046936
##
## var ratio (Tr/Co)..... 0.98911
## T-test p-value........ 0.3873
##
##
## ***** (V3) Christian *****
## before matching:
## mean treatment........ 0.9391
## mean control.......... 0.94915
## std mean diff......... -4.1958
##
## mean raw eQQ diff..... 0.016949
## med raw eQQ diff..... 0
## max raw eQQ diff..... 1
##
## mean eCDF diff........ 0.005025
## med eCDF diff........ 0.005025
## max eCDF diff........ 0.01005
##
## var ratio (Tr/Co)..... 1.1787
## T-test p-value........ 0.68107
##
##
## ***** (V4) age *****
## before matching:
## mean treatment........ 52.628
## mean control.......... 49.178
## std mean diff......... 38.385
##
## mean raw eQQ diff..... 3.661
## med raw eQQ diff..... 4
## max raw eQQ diff..... 7
##
## mean eCDF diff........ 0.075348
## med eCDF diff........ 0.075538
## max eCDF diff........ 0.17807
##
## var ratio (Tr/Co)..... 0.71552
## T-test p-value........ 0.0020402
## KS Bootstrap p-value.. 0.004
## KS Naive p-value...... 0.0087659
## KS Statistic.......... 0.17807
##
##
## ***** (V5) srvlng *****
## before matching:
## mean treatment........ 8.5865
## mean control.......... 8.7458
## std mean diff......... -2.1085
##
## mean raw eQQ diff..... 0.66949
## med raw eQQ diff..... 0
## max raw eQQ diff..... 5
##
## mean eCDF diff........ 0.017181
## med eCDF diff........ 0.01445
## max eCDF diff........ 0.051608
##
## var ratio (Tr/Co)..... 0.77347
## T-test p-value........ 0.85956
## KS Bootstrap p-value.. 0.782
## KS Naive p-value...... 0.97653
## KS Statistic.......... 0.051608
##
##
## ***** (V6) demvote *****
## before matching:
## mean treatment........ 0.49929
## mean control.......... 0.50602
## std mean diff......... -5.2747
##
## mean raw eQQ diff..... 0.011441
## med raw eQQ diff..... 0.01
## max raw eQQ diff..... 0.08
##
## mean eCDF diff........ 0.015928
## med eCDF diff........ 0.010811
## max eCDF diff........ 0.048512
##
## var ratio (Tr/Co)..... 1.1269
## T-test p-value........ 0.61103
## KS Bootstrap p-value.. 0.932
## KS Naive p-value...... 0.98776
## KS Statistic.......... 0.048512
##
##
## Before Matching Minimum p.value: 0.0020402
## Variable Name(s): age Number(s): 4set.seed(2324)
genout <- GenMatch(Tr = foo$hasgirls, X = cbind(foo$Dems, foo$Repubs, foo$Christian, foo$age, foo$srvlng, foo$demvote), pop.size = 20, nboots = 250)##
##
## Sun Apr 10 22:39:38 2022
## Domains:
## 0.000000e+00 <= X1 <= 1.000000e+03
## 0.000000e+00 <= X2 <= 1.000000e+03
## 0.000000e+00 <= X3 <= 1.000000e+03
## 0.000000e+00 <= X4 <= 1.000000e+03
## 0.000000e+00 <= X5 <= 1.000000e+03
## 0.000000e+00 <= X6 <= 1.000000e+03
##
## Data Type: Floating Point
## Operators (code number, name, population)
## (1) Cloning........................... 5
## (2) Uniform Mutation.................. 2
## (3) Boundary Mutation................. 2
## (4) Non-Uniform Mutation.............. 2
## (5) Polytope Crossover................ 2
## (6) Simple Crossover.................. 2
## (7) Whole Non-Uniform Mutation........ 2
## (8) Heuristic Crossover............... 2
## (9) Local-Minimum Crossover........... 0
##
## SOFT Maximum Number of Generations: 100
## Maximum Nonchanging Generations: 4
## Population size : 20
## Convergence Tolerance: 1.000000e-03
##
## Not Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.
## Not Checking Gradients before Stopping.
## Using Out of Bounds Individuals.
##
## Maximization Problem.
## GENERATION: 0 (initializing the population)
## Lexical Fit..... 3.007141e-01 3.173124e-01 3.173124e-01 4.935434e-01 6.160000e-01 6.533362e-01 7.800000e-01 8.520000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 20, #Total UniqueCount: 20
## var 1:
## best............ 8.988479e+02
## mean............ 4.681714e+02
## variance........ 8.152584e+04
## var 2:
## best............ 2.618078e+02
## mean............ 5.271953e+02
## variance........ 9.003735e+04
## var 3:
## best............ 5.277070e+02
## mean............ 3.349127e+02
## variance........ 8.501048e+04
## var 4:
## best............ 6.312860e+02
## mean............ 4.497491e+02
## variance........ 8.534085e+04
## var 5:
## best............ 2.743422e+02
## mean............ 4.406555e+02
## variance........ 8.114577e+04
## var 6:
## best............ 3.453593e+02
## mean............ 3.835392e+02
## variance........ 7.908836e+04
##
## GENERATION: 1
## Lexical Fit..... 3.173124e-01 3.173124e-01 3.881842e-01 5.223950e-01 5.391942e-01 6.920000e-01 7.760000e-01 7.880000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 32
## var 1:
## best............ 9.746223e+02
## mean............ 6.103488e+02
## variance........ 7.739349e+04
## var 2:
## best............ 1.721019e+02
## mean............ 5.375921e+02
## variance........ 7.486352e+04
## var 3:
## best............ 5.093256e+02
## mean............ 5.084006e+02
## variance........ 3.501109e+04
## var 4:
## best............ 6.423314e+02
## mean............ 6.396202e+02
## variance........ 2.167824e+04
## var 5:
## best............ 2.826821e+02
## mean............ 3.643816e+02
## variance........ 2.952440e+04
## var 6:
## best............ 3.727488e+02
## mean............ 3.101248e+02
## variance........ 4.780059e+04
##
## GENERATION: 2
## Lexical Fit..... 3.173124e-01 3.173124e-01 4.266902e-01 4.306904e-01 6.326717e-01 6.960000e-01 8.120000e-01 8.280000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 44
## var 1:
## best............ 9.235152e+02
## mean............ 8.576748e+02
## variance........ 3.176503e+04
## var 2:
## best............ 3.719707e+02
## mean............ 3.560297e+02
## variance........ 3.383778e+04
## var 3:
## best............ 6.680485e+02
## mean............ 5.933514e+02
## variance........ 2.207705e+04
## var 4:
## best............ 6.926548e+02
## mean............ 6.698812e+02
## variance........ 3.958517e+03
## var 5:
## best............ 2.770571e+02
## mean............ 2.856806e+02
## variance........ 9.771335e+02
## var 6:
## best............ 3.861470e+02
## mean............ 3.443519e+02
## variance........ 1.520541e+04
##
## GENERATION: 3
## Lexical Fit..... 3.173124e-01 3.173124e-01 4.266902e-01 4.306904e-01 6.326717e-01 6.960000e-01 8.120000e-01 8.280000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 10, #Total UniqueCount: 54
## var 1:
## best............ 9.235152e+02
## mean............ 9.006171e+02
## variance........ 1.856535e+04
## var 2:
## best............ 3.719707e+02
## mean............ 3.154049e+02
## variance........ 1.069262e+04
## var 3:
## best............ 6.680485e+02
## mean............ 6.417026e+02
## variance........ 1.102847e+04
## var 4:
## best............ 6.926548e+02
## mean............ 6.730033e+02
## variance........ 2.554493e+03
## var 5:
## best............ 2.770571e+02
## mean............ 2.979630e+02
## variance........ 5.964492e+03
## var 6:
## best............ 3.861470e+02
## mean............ 3.956177e+02
## variance........ 3.962419e+03
##
## GENERATION: 4
## Lexical Fit..... 3.173124e-01 3.173124e-01 4.266902e-01 4.306904e-01 6.326717e-01 7.160000e-01 7.760000e-01 7.920000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 66
## var 1:
## best............ 9.233613e+02
## mean............ 9.167202e+02
## variance........ 4.390513e+03
## var 2:
## best............ 3.720204e+02
## mean............ 3.387525e+02
## variance........ 4.581158e+03
## var 3:
## best............ 6.679467e+02
## mean............ 6.238810e+02
## variance........ 1.773897e+04
## var 4:
## best............ 6.925774e+02
## mean............ 6.609378e+02
## variance........ 1.392701e+04
## var 5:
## best............ 2.770402e+02
## mean............ 2.796798e+02
## variance........ 7.359864e+01
## var 6:
## best............ 3.860611e+02
## mean............ 3.825918e+02
## variance........ 3.136723e+01
##
## GENERATION: 5
## Lexical Fit..... 3.173124e-01 3.173124e-01 4.266902e-01 4.306904e-01 6.326717e-01 7.160000e-01 7.760000e-01 7.920000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 78
## var 1:
## best............ 9.233613e+02
## mean............ 8.559007e+02
## variance........ 1.157566e+04
## var 2:
## best............ 3.720204e+02
## mean............ 3.665411e+02
## variance........ 1.115315e+03
## var 3:
## best............ 6.679467e+02
## mean............ 6.541262e+02
## variance........ 2.048069e+03
## var 4:
## best............ 6.925774e+02
## mean............ 6.843354e+02
## variance........ 4.898882e+03
## var 5:
## best............ 2.770402e+02
## mean............ 2.935772e+02
## variance........ 1.751985e+03
## var 6:
## best............ 3.860611e+02
## mean............ 3.894589e+02
## variance........ 2.200047e+02
##
## GENERATION: 6
## Lexical Fit..... 3.173124e-01 3.173124e-01 4.266902e-01 4.306904e-01 6.326717e-01 7.400000e-01 7.480000e-01 8.080000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 11, #Total UniqueCount: 89
## var 1:
## best............ 9.233613e+02
## mean............ 8.752080e+02
## variance........ 9.967567e+03
## var 2:
## best............ 3.720204e+02
## mean............ 3.700676e+02
## variance........ 2.472346e+02
## var 3:
## best............ 6.680383e+02
## mean............ 6.833747e+02
## variance........ 2.023081e+03
## var 4:
## best............ 6.926471e+02
## mean............ 6.985471e+02
## variance........ 6.472662e+02
## var 5:
## best............ 2.770554e+02
## mean............ 2.706025e+02
## variance........ 7.690241e+02
## var 6:
## best............ 3.861384e+02
## mean............ 3.861674e+02
## variance........ 6.054876e+03
##
## GENERATION: 7
## Lexical Fit..... 3.173124e-01 3.173124e-01 4.266902e-01 4.306904e-01 6.326717e-01 7.400000e-01 7.480000e-01 8.080000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 101
## var 1:
## best............ 9.233613e+02
## mean............ 9.122790e+02
## variance........ 4.175793e+03
## var 2:
## best............ 3.720204e+02
## mean............ 3.653292e+02
## variance........ 4.471450e+02
## var 3:
## best............ 6.680383e+02
## mean............ 6.830124e+02
## variance........ 1.522754e+03
## var 4:
## best............ 6.926471e+02
## mean............ 6.996183e+02
## variance........ 1.415870e+03
## var 5:
## best............ 2.770554e+02
## mean............ 2.902830e+02
## variance........ 2.863161e+03
## var 6:
## best............ 3.861384e+02
## mean............ 4.131607e+02
## variance........ 5.451677e+03
##
## GENERATION: 8
## Lexical Fit..... 3.173124e-01 3.173124e-01 4.266902e-01 4.306904e-01 6.326717e-01 7.400000e-01 7.480000e-01 8.080000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 11, #Total UniqueCount: 112
## var 1:
## best............ 9.233613e+02
## mean............ 9.117857e+02
## variance........ 3.434609e+03
## var 2:
## best............ 3.720204e+02
## mean............ 3.867187e+02
## variance........ 4.718236e+03
## var 3:
## best............ 6.680383e+02
## mean............ 6.774556e+02
## variance........ 1.605715e+03
## var 4:
## best............ 6.926471e+02
## mean............ 6.702609e+02
## variance........ 1.533991e+04
## var 5:
## best............ 2.770554e+02
## mean............ 2.905452e+02
## variance........ 2.377334e+03
## var 6:
## best............ 3.861384e+02
## mean............ 4.217108e+02
## variance........ 1.553579e+04
##
## GENERATION: 9
## Lexical Fit..... 3.173124e-01 3.173124e-01 4.266902e-01 4.306904e-01 6.326717e-01 7.400000e-01 7.480000e-01 8.080000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 124
## var 1:
## best............ 9.233613e+02
## mean............ 8.891062e+02
## variance........ 2.296431e+04
## var 2:
## best............ 3.720204e+02
## mean............ 3.733970e+02
## variance........ 4.637219e+01
## var 3:
## best............ 6.680383e+02
## mean............ 6.716955e+02
## variance........ 8.017481e+01
## var 4:
## best............ 6.926471e+02
## mean............ 6.881312e+02
## variance........ 1.509201e+03
## var 5:
## best............ 2.770554e+02
## mean............ 2.912030e+02
## variance........ 1.092848e+03
## var 6:
## best............ 3.861384e+02
## mean............ 3.798968e+02
## variance........ 3.811224e+02
##
## GENERATION: 10
## Lexical Fit..... 3.173124e-01 3.173124e-01 4.266902e-01 4.306904e-01 6.326717e-01 7.400000e-01 7.480000e-01 8.080000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 11, #Total UniqueCount: 135
## var 1:
## best............ 9.233613e+02
## mean............ 9.139450e+02
## variance........ 1.564568e+03
## var 2:
## best............ 3.720204e+02
## mean............ 3.754320e+02
## variance........ 1.614096e+03
## var 3:
## best............ 6.680383e+02
## mean............ 6.725896e+02
## variance........ 2.024168e+02
## var 4:
## best............ 6.926471e+02
## mean............ 6.972219e+02
## variance........ 1.641362e+03
## var 5:
## best............ 2.770554e+02
## mean............ 2.851375e+02
## variance........ 1.376431e+03
## var 6:
## best............ 3.861384e+02
## mean............ 3.969678e+02
## variance........ 1.248760e+03
##
## GENERATION: 11
## Lexical Fit..... 3.173124e-01 3.173124e-01 4.266902e-01 4.306904e-01 6.326717e-01 7.400000e-01 7.480000e-01 8.080000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 11, #Total UniqueCount: 146
## var 1:
## best............ 9.233613e+02
## mean............ 9.241409e+02
## variance........ 8.301958e+00
## var 2:
## best............ 3.720204e+02
## mean............ 3.736094e+02
## variance........ 8.330237e+02
## var 3:
## best............ 6.680383e+02
## mean............ 6.377156e+02
## variance........ 5.788900e+03
## var 4:
## best............ 6.926471e+02
## mean............ 6.824281e+02
## variance........ 5.288418e+03
## var 5:
## best............ 2.770554e+02
## mean............ 2.714709e+02
## variance........ 5.075078e+03
## var 6:
## best............ 3.861384e+02
## mean............ 3.857805e+02
## variance........ 2.346375e+00
##
## 'wait.generations' limit reached.
## No significant improvement in 4 generations.
##
## Solution Lexical Fitness Value:
## 3.173124e-01 3.173124e-01 4.266902e-01 4.306904e-01 6.326717e-01 7.400000e-01 7.480000e-01 8.080000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
##
## Parameters at the Solution:
##
## X[ 1] : 9.233613e+02
## X[ 2] : 3.720204e+02
## X[ 3] : 6.680383e+02
## X[ 4] : 6.926471e+02
## X[ 5] : 2.770554e+02
## X[ 6] : 3.861384e+02
##
## Solution Found Generation 6
## Number of Generations Run 11
##
## Sun Apr 10 22:39:59 2022
## Total run time : 0 hours 0 minutes and 21 secondsmout <- Match(Tr = foo$hasgirls, X = cbind(foo$Dems, foo$Repubs, foo$Christian, foo$age, foo$srvlng, foo$demvote), Weight.matrix = genout)
mb_after <- MatchBalance(hasgirls ~ Dems + Repubs + Christian + age + srvlng + demvote, data = foo, match.out = mout)##
## ***** (V1) Dems *****
## Before Matching After Matching
## mean treatment........ 0.45833 0.45833
## mean control.......... 0.50847 0.46154
## std mean diff......... -10.047 -0.64223
##
## mean raw eQQ diff..... 0.050847 0.0032051
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 1
##
## mean eCDF diff........ 0.025071 0.0016026
## med eCDF diff........ 0.025071 0.0016026
## max eCDF diff........ 0.050141 0.0032051
##
## var ratio (Tr/Co)..... 0.98809 0.99897
## T-test p-value........ 0.35571 0.31731
##
##
## ***** (V2) Repubs *****
## Before Matching After Matching
## mean treatment........ 0.53846 0.53846
## mean control.......... 0.49153 0.53846
## std mean diff......... 9.4 0
##
## mean raw eQQ diff..... 0.042373 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 0
##
## mean eCDF diff........ 0.023468 0
## med eCDF diff........ 0.023468 0
## max eCDF diff........ 0.046936 0
##
## var ratio (Tr/Co)..... 0.98911 1
## T-test p-value........ 0.3873 1
##
##
## ***** (V3) Christian *****
## Before Matching After Matching
## mean treatment........ 0.9391 0.9391
## mean control.......... 0.94915 0.9391
## std mean diff......... -4.1958 0
##
## mean raw eQQ diff..... 0.016949 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 0
##
## mean eCDF diff........ 0.005025 0
## med eCDF diff........ 0.005025 0
## max eCDF diff........ 0.01005 0
##
## var ratio (Tr/Co)..... 1.1787 1
## T-test p-value........ 0.68107 1
##
##
## ***** (V4) age *****
## Before Matching After Matching
## mean treatment........ 52.628 52.628
## mean control.......... 49.178 52.506
## std mean diff......... 38.385 1.355
##
## mean raw eQQ diff..... 3.661 0.58974
## med raw eQQ diff..... 4 1
## max raw eQQ diff..... 7 4
##
## mean eCDF diff........ 0.075348 0.012821
## med eCDF diff........ 0.075538 0.0064103
## max eCDF diff........ 0.17807 0.044872
##
## var ratio (Tr/Co)..... 0.71552 1.0074
## T-test p-value........ 0.0020402 0.43069
## KS Bootstrap p-value.. 0.004 0.798
## KS Naive p-value...... 0.0087659 0.91194
## KS Statistic.......... 0.17807 0.044872
##
##
## ***** (V5) srvlng *****
## Before Matching After Matching
## mean treatment........ 8.5865 8.5865
## mean control.......... 8.7458 8.7179
## std mean diff......... -2.1085 -1.7401
##
## mean raw eQQ diff..... 0.66949 0.45192
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 5 9
##
## mean eCDF diff........ 0.017181 0.012204
## med eCDF diff........ 0.01445 0.0080128
## max eCDF diff........ 0.051608 0.044872
##
## var ratio (Tr/Co)..... 0.77347 0.94705
## T-test p-value........ 0.85956 0.42669
## KS Bootstrap p-value.. 0.788 0.672
## KS Naive p-value...... 0.97653 0.91194
## KS Statistic.......... 0.051608 0.044872
##
##
## ***** (V6) demvote *****
## Before Matching After Matching
## mean treatment........ 0.49929 0.49929
## mean control.......... 0.50602 0.49811
## std mean diff......... -5.2747 0.93056
##
## mean raw eQQ diff..... 0.011441 0.010032
## med raw eQQ diff..... 0.01 0.01
## max raw eQQ diff..... 0.08 0.08
##
## mean eCDF diff........ 0.015928 0.015161
## med eCDF diff........ 0.010811 0.0096154
## max eCDF diff........ 0.048512 0.044872
##
## var ratio (Tr/Co)..... 1.1269 1.1572
## T-test p-value........ 0.61103 0.63267
## KS Bootstrap p-value.. 0.936 0.838
## KS Naive p-value...... 0.98776 0.91194
## KS Statistic.......... 0.048512 0.044872
##
##
## Before Matching Minimum p.value: 0.0020402
## Variable Name(s): age Number(s): 4
##
## After Matching Minimum p.value: 0.31731
## Variable Name(s): Dems Number(s): 1genout_withM2 <- GenMatch(Tr = foo$hasgirls, X = cbind(foo$Dems, foo$Repubs, foo$Christian, foo$age, foo$srvlng, foo$demvote), pop.size = 20, nboots = 250, M = 2)##
##
## Sun Apr 10 22:40:00 2022
## Domains:
## 0.000000e+00 <= X1 <= 1.000000e+03
## 0.000000e+00 <= X2 <= 1.000000e+03
## 0.000000e+00 <= X3 <= 1.000000e+03
## 0.000000e+00 <= X4 <= 1.000000e+03
## 0.000000e+00 <= X5 <= 1.000000e+03
## 0.000000e+00 <= X6 <= 1.000000e+03
##
## Data Type: Floating Point
## Operators (code number, name, population)
## (1) Cloning........................... 5
## (2) Uniform Mutation.................. 2
## (3) Boundary Mutation................. 2
## (4) Non-Uniform Mutation.............. 2
## (5) Polytope Crossover................ 2
## (6) Simple Crossover.................. 2
## (7) Whole Non-Uniform Mutation........ 2
## (8) Heuristic Crossover............... 2
## (9) Local-Minimum Crossover........... 0
##
## SOFT Maximum Number of Generations: 100
## Maximum Nonchanging Generations: 4
## Population size : 20
## Convergence Tolerance: 1.000000e-03
##
## Not Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.
## Not Checking Gradients before Stopping.
## Using Out of Bounds Individuals.
##
## Maximization Problem.
## GENERATION: 0 (initializing the population)
## Lexical Fit..... 1.217810e-01 1.440000e-01 1.569680e-01 1.569680e-01 1.760000e-01 4.796777e-01 4.796777e-01 5.991101e-01 6.080000e-01 6.625970e-01 1.000000e+00 1.000000e+00
## #unique......... 20, #Total UniqueCount: 20
## var 1:
## best............ 4.787857e+02
## mean............ 4.421109e+02
## variance........ 1.239736e+05
## var 2:
## best............ 1.053834e+02
## mean............ 3.374058e+02
## variance........ 8.312254e+04
## var 3:
## best............ 8.531904e+02
## mean............ 4.615873e+02
## variance........ 8.575886e+04
## var 4:
## best............ 6.637632e+02
## mean............ 4.612241e+02
## variance........ 5.406375e+04
## var 5:
## best............ 9.450033e+01
## mean............ 4.367318e+02
## variance........ 8.658821e+04
## var 6:
## best............ 3.141164e+02
## mean............ 5.176758e+02
## variance........ 7.811055e+04
##
## GENERATION: 1
## Lexical Fit..... 1.276416e-01 1.480000e-01 1.569680e-01 1.569680e-01 2.080000e-01 4.796777e-01 4.796777e-01 5.880903e-01 7.080000e-01 7.777129e-01 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 32
## var 1:
## best............ 4.787857e+02
## mean............ 4.562313e+02
## variance........ 7.564409e+04
## var 2:
## best............ 1.053834e+02
## mean............ 2.677724e+02
## variance........ 7.297712e+04
## var 3:
## best............ 8.531904e+02
## mean............ 5.769707e+02
## variance........ 1.184606e+05
## var 4:
## best............ 7.819171e+02
## mean............ 6.012454e+02
## variance........ 4.942781e+04
## var 5:
## best............ 9.450033e+01
## mean............ 3.300927e+02
## variance........ 7.701517e+04
## var 6:
## best............ 3.141164e+02
## mean............ 2.730699e+02
## variance........ 3.350372e+04
##
## GENERATION: 2
## Lexical Fit..... 1.276416e-01 1.569680e-01 1.569680e-01 1.720000e-01 1.920000e-01 4.796777e-01 4.796777e-01 5.880903e-01 7.440000e-01 7.777129e-01 1.000000e+00 1.000000e+00
## #unique......... 9, #Total UniqueCount: 41
## var 1:
## best............ 2.210264e+02
## mean............ 4.275622e+02
## variance........ 1.896408e+04
## var 2:
## best............ 1.053834e+02
## mean............ 2.943579e+02
## variance........ 6.150222e+04
## var 3:
## best............ 8.531904e+02
## mean............ 8.300231e+02
## variance........ 1.475919e+04
## var 4:
## best............ 7.819171e+02
## mean............ 7.148938e+02
## variance........ 7.189240e+03
## var 5:
## best............ 9.450033e+01
## mean............ 2.243822e+02
## variance........ 4.382550e+04
## var 6:
## best............ 3.141164e+02
## mean............ 2.924198e+02
## variance........ 4.785605e+03
##
## GENERATION: 3
## Lexical Fit..... 1.457757e-01 1.480000e-01 1.800000e-01 6.172877e-01 6.172877e-01 7.400000e-01 8.153316e-01 9.026872e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 53
## var 1:
## best............ 9.015035e+01
## mean............ 3.715619e+02
## variance........ 2.072205e+04
## var 2:
## best............ 1.053834e+02
## mean............ 1.478286e+02
## variance........ 2.077822e+04
## var 3:
## best............ 8.531904e+02
## mean............ 8.448531e+02
## variance........ 6.581451e+02
## var 4:
## best............ 8.419091e+02
## mean............ 7.307944e+02
## variance........ 5.175133e+03
## var 5:
## best............ 9.450033e+01
## mean............ 9.001822e+01
## variance........ 3.322537e+02
## var 6:
## best............ 3.141164e+02
## mean............ 3.098669e+02
## variance........ 1.243496e+03
##
## GENERATION: 4
## Lexical Fit..... 1.640000e-01 1.943474e-01 2.520000e-01 2.863712e-01 2.863712e-01 5.226296e-01 5.226296e-01 6.600000e-01 7.764139e-01 9.449636e-01 1.000000e+00 1.000000e+00
## #unique......... 10, #Total UniqueCount: 63
## var 1:
## best............ 4.969046e+01
## mean............ 2.864780e+02
## variance........ 2.974330e+04
## var 2:
## best............ 1.053834e+02
## mean............ 1.329274e+02
## variance........ 1.080344e+04
## var 3:
## best............ 8.531904e+02
## mean............ 8.312898e+02
## variance........ 1.613231e+04
## var 4:
## best............ 8.604555e+02
## mean............ 7.751116e+02
## variance........ 5.965947e+03
## var 5:
## best............ 1.021196e+02
## mean............ 1.251289e+02
## variance........ 1.286621e+04
## var 6:
## best............ 3.141164e+02
## mean............ 3.448520e+02
## variance........ 9.802560e+03
##
## GENERATION: 5
## Lexical Fit..... 1.840000e-01 1.943474e-01 2.720000e-01 2.863712e-01 2.863712e-01 5.226296e-01 5.226296e-01 7.080000e-01 7.764139e-01 9.449636e-01 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 75
## var 1:
## best............ 4.969046e+01
## mean............ 1.420261e+02
## variance........ 2.173175e+04
## var 2:
## best............ 1.053834e+02
## mean............ 1.211457e+02
## variance........ 4.555301e+03
## var 3:
## best............ 8.531904e+02
## mean............ 8.310341e+02
## variance........ 1.237357e+04
## var 4:
## best............ 8.604555e+02
## mean............ 7.947639e+02
## variance........ 2.426786e+04
## var 5:
## best............ 1.021196e+02
## mean............ 9.213941e+01
## variance........ 5.802513e+02
## var 6:
## best............ 3.141164e+02
## mean............ 3.033579e+02
## variance........ 3.831415e+03
##
## GENERATION: 6
## Lexical Fit..... 1.975357e-01 2.320000e-01 2.720000e-01 2.863712e-01 2.863712e-01 5.226296e-01 5.226296e-01 6.880000e-01 7.280932e-01 9.789320e-01 1.000000e+00 1.000000e+00
## #unique......... 10, #Total UniqueCount: 85
## var 1:
## best............ 4.969046e+01
## mean............ 7.874655e+01
## variance........ 8.428700e+03
## var 2:
## best............ 1.053834e+02
## mean............ 1.531207e+02
## variance........ 1.423649e+04
## var 3:
## best............ 8.531904e+02
## mean............ 8.348568e+02
## variance........ 1.080465e+04
## var 4:
## best............ 8.604555e+02
## mean............ 8.481878e+02
## variance........ 1.869834e+03
## var 5:
## best............ 1.022604e+02
## mean............ 1.016482e+02
## variance........ 5.775863e+01
## var 6:
## best............ 3.141164e+02
## mean............ 3.257501e+02
## variance........ 1.307749e+03
##
## GENERATION: 7
## Lexical Fit..... 1.975357e-01 2.320000e-01 2.720000e-01 2.863712e-01 2.863712e-01 5.226296e-01 5.226296e-01 6.880000e-01 7.280932e-01 9.789320e-01 1.000000e+00 1.000000e+00
## #unique......... 9, #Total UniqueCount: 94
## var 1:
## best............ 4.969046e+01
## mean............ 5.265729e+01
## variance........ 1.896575e+02
## var 2:
## best............ 1.053834e+02
## mean............ 1.112971e+02
## variance........ 5.081565e+02
## var 3:
## best............ 8.531904e+02
## mean............ 8.109110e+02
## variance........ 1.668549e+04
## var 4:
## best............ 8.604555e+02
## mean............ 8.512613e+02
## variance........ 1.672911e+03
## var 5:
## best............ 1.022604e+02
## mean............ 9.717612e+01
## variance........ 2.153905e+02
## var 6:
## best............ 3.141164e+02
## mean............ 3.234236e+02
## variance........ 1.421549e+03
##
## GENERATION: 8
## Lexical Fit..... 1.975357e-01 2.440000e-01 2.560000e-01 2.863712e-01 2.863712e-01 5.226296e-01 5.226296e-01 6.480000e-01 7.280932e-01 9.789320e-01 1.000000e+00 1.000000e+00
## #unique......... 11, #Total UniqueCount: 105
## var 1:
## best............ 4.969046e+01
## mean............ 8.737804e+01
## variance........ 1.511847e+04
## var 2:
## best............ 1.053834e+02
## mean............ 1.361306e+02
## variance........ 8.003292e+03
## var 3:
## best............ 9.610316e+02
## mean............ 7.845183e+02
## variance........ 4.169643e+04
## var 4:
## best............ 8.604555e+02
## mean............ 8.334967e+02
## variance........ 1.120493e+04
## var 5:
## best............ 1.022604e+02
## mean............ 1.353815e+02
## variance........ 1.010895e+04
## var 6:
## best............ 3.141164e+02
## mean............ 3.298633e+02
## variance........ 2.384849e+03
##
## GENERATION: 9
## Lexical Fit..... 1.975357e-01 2.440000e-01 2.560000e-01 2.863712e-01 2.863712e-01 5.226296e-01 5.226296e-01 6.480000e-01 7.280932e-01 9.789320e-01 1.000000e+00 1.000000e+00
## #unique......... 13, #Total UniqueCount: 118
## var 1:
## best............ 4.969046e+01
## mean............ 5.867194e+01
## variance........ 1.598794e+03
## var 2:
## best............ 1.053834e+02
## mean............ 1.604772e+02
## variance........ 2.980829e+04
## var 3:
## best............ 9.610316e+02
## mean............ 8.482487e+02
## variance........ 1.625532e+04
## var 4:
## best............ 8.604555e+02
## mean............ 8.653085e+02
## variance........ 1.878150e+02
## var 5:
## best............ 1.022604e+02
## mean............ 1.421845e+02
## variance........ 9.469128e+03
## var 6:
## best............ 3.141164e+02
## mean............ 3.051303e+02
## variance........ 8.177305e+02
##
## GENERATION: 10
## Lexical Fit..... 1.975357e-01 2.440000e-01 2.560000e-01 2.863712e-01 2.863712e-01 5.226296e-01 5.226296e-01 6.480000e-01 7.280932e-01 9.789320e-01 1.000000e+00 1.000000e+00
## #unique......... 10, #Total UniqueCount: 128
## var 1:
## best............ 4.969046e+01
## mean............ 7.309218e+01
## variance........ 1.055539e+04
## var 2:
## best............ 1.053834e+02
## mean............ 1.674024e+02
## variance........ 2.870194e+04
## var 3:
## best............ 9.610316e+02
## mean............ 8.730899e+02
## variance........ 2.204151e+04
## var 4:
## best............ 8.604555e+02
## mean............ 8.339089e+02
## variance........ 7.469210e+03
## var 5:
## best............ 1.022604e+02
## mean............ 1.077536e+02
## variance........ 7.004114e+02
## var 6:
## best............ 3.141164e+02
## mean............ 3.213868e+02
## variance........ 1.735236e+03
##
## GENERATION: 11
## Lexical Fit..... 1.975357e-01 2.440000e-01 2.560000e-01 2.863712e-01 2.863712e-01 5.226296e-01 5.226296e-01 6.480000e-01 7.280932e-01 9.789320e-01 1.000000e+00 1.000000e+00
## #unique......... 9, #Total UniqueCount: 137
## var 1:
## best............ 4.969046e+01
## mean............ 8.314163e+01
## variance........ 8.288561e+03
## var 2:
## best............ 1.053834e+02
## mean............ 1.429799e+02
## variance........ 1.662663e+04
## var 3:
## best............ 9.610316e+02
## mean............ 9.025552e+02
## variance........ 1.289274e+04
## var 4:
## best............ 8.604555e+02
## mean............ 8.607457e+02
## variance........ 1.649183e+02
## var 5:
## best............ 1.022604e+02
## mean............ 1.529418e+02
## variance........ 1.848288e+04
## var 6:
## best............ 3.141164e+02
## mean............ 3.145325e+02
## variance........ 5.019916e+01
##
## GENERATION: 12
## Lexical Fit..... 1.975357e-01 2.440000e-01 2.560000e-01 2.863712e-01 2.863712e-01 5.226296e-01 5.226296e-01 6.480000e-01 7.280932e-01 9.789320e-01 1.000000e+00 1.000000e+00
## #unique......... 7, #Total UniqueCount: 144
## var 1:
## best............ 4.969046e+01
## mean............ 5.784924e+01
## variance........ 1.259940e+03
## var 2:
## best............ 1.053834e+02
## mean............ 1.092174e+02
## variance........ 4.662368e+02
## var 3:
## best............ 9.610316e+02
## mean............ 9.405765e+02
## variance........ 1.974704e+03
## var 4:
## best............ 8.604555e+02
## mean............ 8.406489e+02
## variance........ 3.930331e+03
## var 5:
## best............ 1.022604e+02
## mean............ 1.180278e+02
## variance........ 2.543079e+03
## var 6:
## best............ 3.141164e+02
## mean............ 3.272319e+02
## variance........ 1.990028e+03
##
## GENERATION: 13
## Lexical Fit..... 1.975357e-01 2.440000e-01 2.560000e-01 2.863712e-01 2.863712e-01 5.226296e-01 5.226296e-01 6.480000e-01 7.280932e-01 9.789320e-01 1.000000e+00 1.000000e+00
## #unique......... 8, #Total UniqueCount: 152
## var 1:
## best............ 4.969046e+01
## mean............ 6.455565e+01
## variance........ 2.531087e+03
## var 2:
## best............ 1.053834e+02
## mean............ 1.099929e+02
## variance........ 4.735317e+02
## var 3:
## best............ 9.610316e+02
## mean............ 9.452990e+02
## variance........ 4.270057e+03
## var 4:
## best............ 8.604555e+02
## mean............ 8.481815e+02
## variance........ 1.395909e+03
## var 5:
## best............ 1.022604e+02
## mean............ 1.000700e+02
## variance........ 3.852534e+01
## var 6:
## best............ 3.141164e+02
## mean............ 3.151712e+02
## variance........ 2.256832e+01
##
## 'wait.generations' limit reached.
## No significant improvement in 4 generations.
##
## Solution Lexical Fitness Value:
## 1.975357e-01 2.440000e-01 2.560000e-01 2.863712e-01 2.863712e-01 5.226296e-01 5.226296e-01 6.480000e-01 7.280932e-01 9.789320e-01 1.000000e+00 1.000000e+00
##
## Parameters at the Solution:
##
## X[ 1] : 4.969046e+01
## X[ 2] : 1.053834e+02
## X[ 3] : 9.610316e+02
## X[ 4] : 8.604555e+02
## X[ 5] : 1.022604e+02
## X[ 6] : 3.141164e+02
##
## Solution Found Generation 8
## Number of Generations Run 13
##
## Sun Apr 10 22:40:32 2022
## Total run time : 0 hours 0 minutes and 32 secondsmout_withM2 <- Match(Tr = foo$hasgirls, X = cbind(foo$Dems, foo$Repubs, foo$Christian, foo$age, foo$srvlng, foo$demvote), Weight.matrix = genout_withM2, M = 2)
mb_withM2 <- MatchBalance(hasgirls ~ Dems + Repubs + Christian + age + srvlng + demvote, data = foo, match.out = mout_withM2)##
## ***** (V1) Dems *****
## Before Matching After Matching
## mean treatment........ 0.45833 0.45833
## mean control.......... 0.50847 0.45353
## std mean diff......... -10.047 0.96335
##
## mean raw eQQ diff..... 0.050847 0.0048
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 1
##
## mean eCDF diff........ 0.025071 0.0024
## med eCDF diff........ 0.025071 0.0024
## max eCDF diff........ 0.050141 0.0048
##
## var ratio (Tr/Co)..... 0.98809 1.0017
## T-test p-value........ 0.35571 0.52263
##
##
## ***** (V2) Repubs *****
## Before Matching After Matching
## mean treatment........ 0.53846 0.53846
## mean control.......... 0.49153 0.54647
## std mean diff......... 9.4 -1.6047
##
## mean raw eQQ diff..... 0.042373 0.008
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 1
##
## mean eCDF diff........ 0.023468 0.004
## med eCDF diff........ 0.023468 0.004
## max eCDF diff........ 0.046936 0.008
##
## var ratio (Tr/Co)..... 0.98911 1.0027
## T-test p-value........ 0.3873 0.28637
##
##
## ***** (V3) Christian *****
## Before Matching After Matching
## mean treatment........ 0.9391 0.9391
## mean control.......... 0.94915 0.9391
## std mean diff......... -4.1958 0
##
## mean raw eQQ diff..... 0.016949 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 0
##
## mean eCDF diff........ 0.005025 0
## med eCDF diff........ 0.005025 0
## max eCDF diff........ 0.01005 0
##
## var ratio (Tr/Co)..... 1.1787 1
## T-test p-value........ 0.68107 1
##
##
## ***** (V4) age *****
## Before Matching After Matching
## mean treatment........ 52.628 52.628
## mean control.......... 49.178 52.431
## std mean diff......... 38.385 2.193
##
## mean raw eQQ diff..... 3.661 0.616
## med raw eQQ diff..... 4 1
## max raw eQQ diff..... 7 7
##
## mean eCDF diff........ 0.075348 0.013043
## med eCDF diff........ 0.075538 0.008
## max eCDF diff........ 0.17807 0.0512
##
## var ratio (Tr/Co)..... 0.71552 1.0449
## T-test p-value........ 0.0020402 0.19754
## KS Bootstrap p-value.. 0.004 0.258
## KS Naive p-value...... 0.0087659 0.38574
## KS Statistic.......... 0.17807 0.0512
##
##
## ***** (V5) srvlng *****
## Before Matching After Matching
## mean treatment........ 8.5865 8.5865
## mean control.......... 8.7458 8.6768
## std mean diff......... -2.1085 -1.1955
##
## mean raw eQQ diff..... 0.66949 0.4016
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 5 9
##
## mean eCDF diff........ 0.017181 0.01043
## med eCDF diff........ 0.01445 0.008
## max eCDF diff........ 0.051608 0.032
##
## var ratio (Tr/Co)..... 0.77347 1.0244
## T-test p-value........ 0.85956 0.72809
## KS Bootstrap p-value.. 0.792 0.634
## KS Naive p-value...... 0.97653 0.90621
## KS Statistic.......... 0.051608 0.032
##
##
## ***** (V6) demvote *****
## Before Matching After Matching
## mean treatment........ 0.49929 0.49929
## mean control.......... 0.50602 0.49937
## std mean diff......... -5.2747 -0.062875
##
## mean raw eQQ diff..... 0.011441 0.010864
## med raw eQQ diff..... 0.01 0.01
## max raw eQQ diff..... 0.08 0.08
##
## mean eCDF diff........ 0.015928 0.016432
## med eCDF diff........ 0.010811 0.0144
## max eCDF diff........ 0.048512 0.056
##
## var ratio (Tr/Co)..... 1.1269 1.1832
## T-test p-value........ 0.61103 0.97893
## KS Bootstrap p-value.. 0.924 0.186
## KS Naive p-value...... 0.98776 0.28096
## KS Statistic.......... 0.048512 0.056
##
##
## Before Matching Minimum p.value: 0.0020402
## Variable Name(s): age Number(s): 4
##
## After Matching Minimum p.value: 0.186
## Variable Name(s): demvote Number(s): 6genout_withM3 <- GenMatch(Tr = foo$hasgirls, X = cbind(foo$Dems, foo$Repubs, foo$Christian, foo$age, foo$srvlng, foo$demvote), pop.size = 20, nboots = 250, M = 3)##
##
## Sun Apr 10 22:40:33 2022
## Domains:
## 0.000000e+00 <= X1 <= 1.000000e+03
## 0.000000e+00 <= X2 <= 1.000000e+03
## 0.000000e+00 <= X3 <= 1.000000e+03
## 0.000000e+00 <= X4 <= 1.000000e+03
## 0.000000e+00 <= X5 <= 1.000000e+03
## 0.000000e+00 <= X6 <= 1.000000e+03
##
## Data Type: Floating Point
## Operators (code number, name, population)
## (1) Cloning........................... 5
## (2) Uniform Mutation.................. 2
## (3) Boundary Mutation................. 2
## (4) Non-Uniform Mutation.............. 2
## (5) Polytope Crossover................ 2
## (6) Simple Crossover.................. 2
## (7) Whole Non-Uniform Mutation........ 2
## (8) Heuristic Crossover............... 2
## (9) Local-Minimum Crossover........... 0
##
## SOFT Maximum Number of Generations: 100
## Maximum Nonchanging Generations: 4
## Population size : 20
## Convergence Tolerance: 1.000000e-03
##
## Not Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.
## Not Checking Gradients before Stopping.
## Using Out of Bounds Individuals.
##
## Maximization Problem.
## GENERATION: 0 (initializing the population)
## Lexical Fit..... 1.621419e-02 2.000000e-02 7.200000e-02 1.569680e-01 1.569680e-01 4.387264e-01 4.387264e-01 5.240000e-01 6.705279e-01 7.787449e-01 1.000000e+00 1.000000e+00
## #unique......... 20, #Total UniqueCount: 20
## var 1:
## best............ 5.753846e+02
## mean............ 5.555705e+02
## variance........ 1.102707e+05
## var 2:
## best............ 3.995648e+02
## mean............ 4.124175e+02
## variance........ 8.398474e+04
## var 3:
## best............ 8.339054e+02
## mean............ 5.485548e+02
## variance........ 7.841503e+04
## var 4:
## best............ 7.859569e+02
## mean............ 4.760546e+02
## variance........ 8.926673e+04
## var 5:
## best............ 4.739673e+01
## mean............ 5.189730e+02
## variance........ 9.563991e+04
## var 6:
## best............ 6.958361e+02
## mean............ 4.959198e+02
## variance........ 7.648976e+04
##
## GENERATION: 1
## Lexical Fit..... 1.621419e-02 2.000000e-02 7.200000e-02 1.569680e-01 1.569680e-01 4.387264e-01 4.387264e-01 5.240000e-01 6.705279e-01 7.787449e-01 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 32
## var 1:
## best............ 5.753846e+02
## mean............ 4.270557e+02
## variance........ 6.575962e+04
## var 2:
## best............ 3.995648e+02
## mean............ 3.622430e+02
## variance........ 6.216538e+04
## var 3:
## best............ 8.339054e+02
## mean............ 7.217667e+02
## variance........ 3.473626e+04
## var 4:
## best............ 7.859569e+02
## mean............ 6.913169e+02
## variance........ 3.300717e+04
## var 5:
## best............ 4.739673e+01
## mean............ 4.866323e+02
## variance........ 1.101391e+05
## var 6:
## best............ 6.958361e+02
## mean............ 4.049157e+02
## variance........ 8.584864e+04
##
## GENERATION: 2
## Lexical Fit..... 2.800000e-02 2.846634e-02 6.400000e-02 1.060246e-01 1.200000e-01 5.075069e-01 7.390625e-01 7.390625e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 11, #Total UniqueCount: 43
## var 1:
## best............ 1.094605e+02
## mean............ 4.790964e+02
## variance........ 3.169821e+04
## var 2:
## best............ 4.024473e+01
## mean............ 3.345182e+02
## variance........ 2.708718e+04
## var 3:
## best............ 6.647515e+02
## mean............ 7.296513e+02
## variance........ 1.739375e+04
## var 4:
## best............ 6.837231e+02
## mean............ 7.523776e+02
## variance........ 1.212423e+04
## var 5:
## best............ 8.705133e+02
## mean............ 2.094407e+02
## variance........ 8.821512e+04
## var 6:
## best............ 1.175579e+01
## mean............ 4.990478e+02
## variance........ 7.302942e+04
##
## GENERATION: 3
## Lexical Fit..... 3.297033e-02 5.600000e-02 6.800000e-02 1.400000e-01 2.204882e-01 2.204882e-01 2.387960e-01 2.863252e-01 5.128852e-01 5.128852e-01 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 55
## var 1:
## best............ 5.532382e+02
## mean............ 3.628030e+02
## variance........ 4.843976e+04
## var 2:
## best............ 3.472824e+01
## mean............ 1.863646e+02
## variance........ 2.524689e+04
## var 3:
## best............ 6.264175e+02
## mean............ 7.369862e+02
## variance........ 5.547301e+03
## var 4:
## best............ 7.234631e+02
## mean............ 7.352021e+02
## variance........ 8.223759e+03
## var 5:
## best............ 2.720762e+02
## mean............ 3.858978e+02
## variance........ 1.330678e+05
## var 6:
## best............ 8.651452e+00
## mean............ 2.626411e+02
## variance........ 7.070853e+04
##
## GENERATION: 4
## Lexical Fit..... 4.888926e-02 8.000000e-02 1.040000e-01 1.080000e-01 1.251284e-01 2.609555e-01 2.609555e-01 4.054981e-01 4.054981e-01 6.974754e-01 1.000000e+00 1.000000e+00
## #unique......... 13, #Total UniqueCount: 68
## var 1:
## best............ 1.063671e+02
## mean............ 4.081230e+02
## variance........ 5.232050e+04
## var 2:
## best............ 4.979002e+00
## mean............ 7.663751e+01
## variance........ 8.946600e+03
## var 3:
## best............ 6.636285e+02
## mean............ 6.640422e+02
## variance........ 2.944609e+03
## var 4:
## best............ 6.825330e+02
## mean............ 7.075752e+02
## variance........ 3.097057e+03
## var 5:
## best............ 8.759782e+02
## mean............ 4.606583e+02
## variance........ 1.167103e+05
## var 6:
## best............ 7.214021e+00
## mean............ 1.224933e+02
## variance........ 6.240560e+04
##
## GENERATION: 5
## Lexical Fit..... 4.921943e-02 7.600000e-02 7.600000e-02 1.000000e-01 1.255774e-01 2.609555e-01 2.609555e-01 4.054981e-01 4.054981e-01 6.770107e-01 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 80
## var 1:
## best............ 1.063671e+02
## mean............ 2.376657e+02
## variance........ 4.094935e+04
## var 2:
## best............ 4.979002e+00
## mean............ 8.724578e+01
## variance........ 2.638443e+04
## var 3:
## best............ 6.636285e+02
## mean............ 6.420159e+02
## variance........ 1.711728e+03
## var 4:
## best............ 6.825330e+02
## mean............ 6.586449e+02
## variance........ 6.013599e+03
## var 5:
## best............ 9.047249e+02
## mean............ 7.068684e+02
## variance........ 7.510790e+04
## var 6:
## best............ 7.214021e+00
## mean............ 5.386398e+01
## variance........ 1.533817e+04
##
## GENERATION: 6
## Lexical Fit..... 4.921943e-02 7.600000e-02 7.600000e-02 1.000000e-01 1.255774e-01 2.609555e-01 2.609555e-01 4.054981e-01 4.054981e-01 6.770107e-01 1.000000e+00 1.000000e+00
## #unique......... 9, #Total UniqueCount: 89
## var 1:
## best............ 1.063671e+02
## mean............ 2.292796e+02
## variance........ 4.380493e+04
## var 2:
## best............ 4.979002e+00
## mean............ 1.621657e+01
## variance........ 3.219378e+02
## var 3:
## best............ 6.636285e+02
## mean............ 6.551896e+02
## variance........ 4.061753e+03
## var 4:
## best............ 6.825330e+02
## mean............ 6.592471e+02
## variance........ 7.159028e+03
## var 5:
## best............ 9.047249e+02
## mean............ 7.173508e+02
## variance........ 8.769432e+04
## var 6:
## best............ 7.214021e+00
## mean............ 1.212284e+01
## variance........ 6.454829e+02
##
## GENERATION: 7
## Lexical Fit..... 6.716891e-02 9.600000e-02 1.000000e-01 1.166667e-01 1.166667e-01 1.911686e-01 1.911686e-01 2.000000e-01 3.807400e-01 7.240898e-01 1.000000e+00 1.000000e+00
## #unique......... 8, #Total UniqueCount: 97
## var 1:
## best............ 1.063671e+02
## mean............ 1.069118e+02
## variance........ 4.669512e+02
## var 2:
## best............ 4.979002e+00
## mean............ 1.690317e+01
## variance........ 1.442867e+03
## var 3:
## best............ 6.636285e+02
## mean............ 6.540005e+02
## variance........ 5.298288e+03
## var 4:
## best............ 9.473561e+02
## mean............ 6.987704e+02
## variance........ 3.588590e+03
## var 5:
## best............ 9.047249e+02
## mean............ 9.038005e+02
## variance........ 3.907926e+02
## var 6:
## best............ 7.214021e+00
## mean............ 2.185364e+01
## variance........ 4.182589e+03
##
## GENERATION: 8
## Lexical Fit..... 8.181862e-02 9.600000e-02 1.020127e-01 1.020127e-01 1.040000e-01 1.690250e-01 1.690250e-01 2.040000e-01 3.598874e-01 6.977615e-01 1.000000e+00 1.000000e+00
## #unique......... 10, #Total UniqueCount: 107
## var 1:
## best............ 1.063671e+02
## mean............ 1.356750e+02
## variance........ 9.376082e+03
## var 2:
## best............ 4.979002e+00
## mean............ 8.675496e+01
## variance........ 4.034698e+04
## var 3:
## best............ 6.636285e+02
## mean............ 6.551897e+02
## variance........ 1.251519e+03
## var 4:
## best............ 9.905277e+02
## mean............ 7.888536e+02
## variance........ 1.694211e+04
## var 5:
## best............ 9.047249e+02
## mean............ 8.662759e+02
## variance........ 2.782768e+04
## var 6:
## best............ 7.214021e+00
## mean............ 1.601552e+01
## variance........ 7.929939e+02
##
## GENERATION: 9
## Lexical Fit..... 9.600000e-02 9.600000e-02 1.040000e-01 2.192399e-01 2.275353e-01 2.275353e-01 2.914943e-01 2.914943e-01 3.144880e-01 9.155194e-01 1.000000e+00 1.000000e+00
## #unique......... 11, #Total UniqueCount: 118
## var 1:
## best............ 3.075592e+01
## mean............ 8.738985e+01
## variance........ 1.019117e+03
## var 2:
## best............ 4.409500e+00
## mean............ 6.385351e+01
## variance........ 3.200630e+04
## var 3:
## best............ 6.619400e+02
## mean............ 6.508772e+02
## variance........ 1.100414e+04
## var 4:
## best............ 9.409769e+02
## mean............ 8.939482e+02
## variance........ 2.903987e+04
## var 5:
## best............ 9.038527e+02
## mean............ 8.860725e+02
## variance........ 4.488978e+03
## var 6:
## best............ 5.594384e+00
## mean............ 9.307132e+00
## variance........ 1.037578e+02
##
## GENERATION: 10
## Lexical Fit..... 1.200000e-01 1.520000e-01 1.569680e-01 1.569680e-01 1.770186e-01 1.770186e-01 2.280000e-01 2.309052e-01 2.309052e-01 3.008787e-01 4.221285e-01 9.295623e-01
## #unique......... 11, #Total UniqueCount: 129
## var 1:
## best............ 3.075592e+01
## mean............ 1.149816e+02
## variance........ 1.756656e+04
## var 2:
## best............ 4.409500e+00
## mean............ 2.328608e+01
## variance........ 3.234210e+03
## var 3:
## best............ 1.767479e+02
## mean............ 6.420415e+02
## variance........ 1.156782e+04
## var 4:
## best............ 9.409769e+02
## mean............ 9.157817e+02
## variance........ 3.472189e+04
## var 5:
## best............ 9.038527e+02
## mean............ 8.969442e+02
## variance........ 1.656894e+03
## var 6:
## best............ 5.594384e+00
## mean............ 2.744616e+01
## variance........ 2.882705e+03
##
## GENERATION: 11
## Lexical Fit..... 1.200000e-01 1.520000e-01 1.569680e-01 1.569680e-01 1.770186e-01 1.770186e-01 2.280000e-01 2.309052e-01 2.309052e-01 3.008787e-01 4.221285e-01 9.295623e-01
## #unique......... 11, #Total UniqueCount: 140
## var 1:
## best............ 3.075592e+01
## mean............ 4.693015e+01
## variance........ 1.163284e+03
## var 2:
## best............ 4.409500e+00
## mean............ 8.791549e+00
## variance........ 1.811752e+02
## var 3:
## best............ 1.767479e+02
## mean............ 5.393465e+02
## variance........ 3.690845e+04
## var 4:
## best............ 9.409769e+02
## mean............ 9.278445e+02
## variance........ 2.814470e+03
## var 5:
## best............ 9.038527e+02
## mean............ 8.990767e+02
## variance........ 1.117142e+03
## var 6:
## best............ 5.594384e+00
## mean............ 5.251665e+01
## variance........ 2.809062e+04
##
## GENERATION: 12
## Lexical Fit..... 1.200000e-01 1.520000e-01 1.569680e-01 1.569680e-01 1.770186e-01 1.770186e-01 2.280000e-01 2.309052e-01 2.309052e-01 3.008787e-01 4.221285e-01 9.295623e-01
## #unique......... 6, #Total UniqueCount: 146
## var 1:
## best............ 3.075592e+01
## mean............ 3.458910e+01
## variance........ 3.264829e+02
## var 2:
## best............ 4.409500e+00
## mean............ 1.304247e+01
## variance........ 7.257350e+02
## var 3:
## best............ 1.767479e+02
## mean............ 3.278778e+02
## variance........ 4.980355e+04
## var 4:
## best............ 9.409769e+02
## mean............ 9.369355e+02
## variance........ 3.701659e+02
## var 5:
## best............ 9.038527e+02
## mean............ 9.045183e+02
## variance........ 2.114907e+02
## var 6:
## best............ 5.594384e+00
## mean............ 6.014242e+00
## variance........ 3.216356e+00
##
## GENERATION: 13
## Lexical Fit..... 1.320000e-01 1.533062e-01 1.533062e-01 1.569680e-01 1.569680e-01 2.013049e-01 2.013049e-01 2.160000e-01 2.640000e-01 3.635938e-01 3.791716e-01 8.782963e-01
## #unique......... 8, #Total UniqueCount: 154
## var 1:
## best............ 3.075592e+01
## mean............ 7.654629e+01
## variance........ 1.090494e+04
## var 2:
## best............ 4.409500e+00
## mean............ 1.794567e+01
## variance........ 3.061769e+03
## var 3:
## best............ 1.767479e+02
## mean............ 1.976682e+02
## variance........ 8.203072e+03
## var 4:
## best............ 9.671828e+02
## mean............ 9.336125e+02
## variance........ 4.567080e+02
## var 5:
## best............ 9.038527e+02
## mean............ 8.870277e+02
## variance........ 3.663486e+03
## var 6:
## best............ 5.594384e+00
## mean............ 3.545039e+01
## variance........ 1.159189e+04
##
## GENERATION: 14
## Lexical Fit..... 1.320000e-01 1.533062e-01 1.533062e-01 1.569680e-01 1.569680e-01 2.013049e-01 2.013049e-01 2.160000e-01 2.640000e-01 3.635938e-01 3.791716e-01 8.782963e-01
## #unique......... 12, #Total UniqueCount: 166
## var 1:
## best............ 3.075592e+01
## mean............ 3.581525e+01
## variance........ 2.561811e+02
## var 2:
## best............ 4.409500e+00
## mean............ 2.060610e+01
## variance........ 3.409887e+03
## var 3:
## best............ 1.767479e+02
## mean............ 1.716697e+02
## variance........ 1.255790e+02
## var 4:
## best............ 9.671828e+02
## mean............ 9.520914e+02
## variance........ 2.125235e+02
## var 5:
## best............ 9.038527e+02
## mean............ 8.734622e+02
## variance........ 1.098850e+04
## var 6:
## best............ 5.594384e+00
## mean............ 4.054465e+01
## variance........ 1.565574e+04
##
## GENERATION: 15
## Lexical Fit..... 1.320000e-01 1.533062e-01 1.533062e-01 1.569680e-01 1.569680e-01 2.013049e-01 2.013049e-01 2.160000e-01 2.640000e-01 3.635938e-01 3.791716e-01 8.782963e-01
## #unique......... 11, #Total UniqueCount: 177
## var 1:
## best............ 3.075592e+01
## mean............ 4.605072e+01
## variance........ 1.564637e+03
## var 2:
## best............ 4.409500e+00
## mean............ 7.750379e+00
## variance........ 2.136011e+02
## var 3:
## best............ 1.767479e+02
## mean............ 1.933981e+02
## variance........ 8.784576e+03
## var 4:
## best............ 9.671828e+02
## mean............ 9.423585e+02
## variance........ 1.303913e+03
## var 5:
## best............ 9.038527e+02
## mean............ 8.854600e+02
## variance........ 2.879091e+03
## var 6:
## best............ 5.594384e+00
## mean............ 4.717376e+01
## variance........ 1.670557e+04
##
## GENERATION: 16
## Lexical Fit..... 1.320000e-01 1.533062e-01 1.533062e-01 1.569680e-01 1.569680e-01 2.013049e-01 2.013049e-01 2.160000e-01 2.640000e-01 3.635938e-01 3.791716e-01 8.782963e-01
## #unique......... 8, #Total UniqueCount: 185
## var 1:
## best............ 3.075592e+01
## mean............ 3.842214e+01
## variance........ 5.055084e+02
## var 2:
## best............ 4.409500e+00
## mean............ 2.231734e+01
## variance........ 1.889943e+03
## var 3:
## best............ 1.767479e+02
## mean............ 1.911297e+02
## variance........ 4.471888e+03
## var 4:
## best............ 9.671828e+02
## mean............ 9.594125e+02
## variance........ 1.349411e+02
## var 5:
## best............ 9.038527e+02
## mean............ 8.919181e+02
## variance........ 8.566335e+02
## var 6:
## best............ 5.594384e+00
## mean............ 1.783102e+01
## variance........ 2.833980e+03
##
## GENERATION: 17
## Lexical Fit..... 1.320000e-01 1.533062e-01 1.533062e-01 1.569680e-01 1.569680e-01 2.013049e-01 2.013049e-01 2.160000e-01 2.640000e-01 3.635938e-01 3.791716e-01 8.782963e-01
## #unique......... 9, #Total UniqueCount: 194
## var 1:
## best............ 3.075592e+01
## mean............ 3.084220e+01
## variance........ 4.472427e+00
## var 2:
## best............ 4.409500e+00
## mean............ 1.596190e+01
## variance........ 1.220704e+03
## var 3:
## best............ 1.767479e+02
## mean............ 2.200967e+02
## variance........ 2.191616e+04
## var 4:
## best............ 9.671828e+02
## mean............ 9.341714e+02
## variance........ 1.717979e+04
## var 5:
## best............ 9.038527e+02
## mean............ 8.923707e+02
## variance........ 3.548442e+03
## var 6:
## best............ 5.594384e+00
## mean............ 1.416296e+01
## variance........ 1.404137e+03
##
## GENERATION: 18
## Lexical Fit..... 1.320000e-01 1.533062e-01 1.533062e-01 1.569680e-01 1.569680e-01 2.013049e-01 2.013049e-01 2.160000e-01 2.640000e-01 3.635938e-01 3.791716e-01 8.782963e-01
## #unique......... 6, #Total UniqueCount: 200
## var 1:
## best............ 3.075592e+01
## mean............ 3.209304e+01
## variance........ 7.798555e+01
## var 2:
## best............ 4.409500e+00
## mean............ 4.572540e+01
## variance........ 1.570474e+04
## var 3:
## best............ 1.767479e+02
## mean............ 1.865372e+02
## variance........ 2.993043e+03
## var 4:
## best............ 9.671828e+02
## mean............ 9.528167e+02
## variance........ 4.522131e+03
## var 5:
## best............ 9.038527e+02
## mean............ 8.756600e+02
## variance........ 7.685604e+03
## var 6:
## best............ 5.594384e+00
## mean............ 5.575285e+00
## variance........ 5.613593e-03
##
## 'wait.generations' limit reached.
## No significant improvement in 4 generations.
##
## Solution Lexical Fitness Value:
## 1.320000e-01 1.533062e-01 1.533062e-01 1.569680e-01 1.569680e-01 2.013049e-01 2.013049e-01 2.160000e-01 2.640000e-01 3.635938e-01 3.791716e-01 8.782963e-01
##
## Parameters at the Solution:
##
## X[ 1] : 3.075592e+01
## X[ 2] : 4.409500e+00
## X[ 3] : 1.767479e+02
## X[ 4] : 9.671828e+02
## X[ 5] : 9.038527e+02
## X[ 6] : 5.594384e+00
##
## Solution Found Generation 13
## Number of Generations Run 18
##
## Sun Apr 10 22:41:27 2022
## Total run time : 0 hours 0 minutes and 54 secondsmout_withM3 <- Match(Tr = foo$hasgirls, X = cbind(foo$Dems, foo$Repubs, foo$Christian, foo$age, foo$srvlng, foo$demvote), Weight.matrix = genout_withM3, M = 3)
mb_withM3 <- MatchBalance(hasgirls ~ Dems + Repubs + Christian + age + srvlng + demvote, data = foo, match.out = mout_withM3)##
## ***** (V1) Dems *****
## Before Matching After Matching
## mean treatment........ 0.45833 0.45833
## mean control.......... 0.50847 0.43002
## std mean diff......... -10.047 5.673
##
## mean raw eQQ diff..... 0.050847 0.027719
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 1
##
## mean eCDF diff........ 0.025071 0.013859
## med eCDF diff........ 0.025071 0.013859
## max eCDF diff........ 0.050141 0.027719
##
## var ratio (Tr/Co)..... 0.98809 1.0129
## T-test p-value........ 0.35571 0.2013
##
##
## ***** (V2) Repubs *****
## Before Matching After Matching
## mean treatment........ 0.53846 0.53846
## mean control.......... 0.49153 0.56998
## std mean diff......... 9.4 -6.312
##
## mean raw eQQ diff..... 0.042373 0.030917
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 1
##
## mean eCDF diff........ 0.023468 0.015458
## med eCDF diff........ 0.023468 0.015458
## max eCDF diff........ 0.046936 0.030917
##
## var ratio (Tr/Co)..... 0.98911 1.0139
## T-test p-value........ 0.3873 0.15331
##
##
## ***** (V3) Christian *****
## Before Matching After Matching
## mean treatment........ 0.9391 0.9391
## mean control.......... 0.94915 0.94551
## std mean diff......... -4.1958 -2.6762
##
## mean raw eQQ diff..... 0.016949 0.0063966
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 1
##
## mean eCDF diff........ 0.005025 0.0031983
## med eCDF diff........ 0.005025 0.0031983
## max eCDF diff........ 0.01005 0.0063966
##
## var ratio (Tr/Co)..... 1.1787 1.1101
## T-test p-value........ 0.68107 0.15697
##
##
## ***** (V4) age *****
## Before Matching After Matching
## mean treatment........ 52.628 52.628
## mean control.......... 49.178 52.493
## std mean diff......... 38.385 1.5036
##
## mean raw eQQ diff..... 3.661 0.54797
## med raw eQQ diff..... 4 1
## max raw eQQ diff..... 7 7
##
## mean eCDF diff........ 0.075348 0.01194
## med eCDF diff........ 0.075538 0.0074627
## max eCDF diff........ 0.17807 0.044776
##
## var ratio (Tr/Co)..... 0.71552 1.0444
## T-test p-value........ 0.0020402 0.37917
## KS Bootstrap p-value.. 0.004 0.25
## KS Naive p-value...... 0.0087659 0.30394
## KS Statistic.......... 0.17807 0.044776
##
##
## ***** (V5) srvlng *****
## Before Matching After Matching
## mean treatment........ 8.5865 8.5865
## mean control.......... 8.7458 8.4674
## std mean diff......... -2.1085 1.5774
##
## mean raw eQQ diff..... 0.66949 0.39232
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 5 9
##
## mean eCDF diff........ 0.017181 0.0099229
## med eCDF diff........ 0.01445 0.0063966
## max eCDF diff........ 0.051608 0.039446
##
## var ratio (Tr/Co)..... 0.77347 1.0436
## T-test p-value........ 0.85956 0.36359
## KS Bootstrap p-value.. 0.77 0.266
## KS Naive p-value...... 0.97653 0.45889
## KS Statistic.......... 0.051608 0.039446
##
##
## ***** (V6) demvote *****
## Before Matching After Matching
## mean treatment........ 0.49929 0.49929
## mean control.......... 0.50602 0.49809
## std mean diff......... -5.2747 0.94314
##
## mean raw eQQ diff..... 0.011441 0.011514
## med raw eQQ diff..... 0.01 0.01
## max raw eQQ diff..... 0.08 0.08
##
## mean eCDF diff........ 0.015928 0.017345
## med eCDF diff........ 0.010811 0.014925
## max eCDF diff........ 0.048512 0.053305
##
## var ratio (Tr/Co)..... 1.1269 1.2317
## T-test p-value........ 0.61103 0.8783
## KS Bootstrap p-value.. 0.946 0.07
## KS Naive p-value...... 0.98776 0.13912
## KS Statistic.......... 0.048512 0.053305
##
##
## Before Matching Minimum p.value: 0.0020402
## Variable Name(s): age Number(s): 4
##
## After Matching Minimum p.value: 0.07
## Variable Name(s): demvote Number(s): 6mout_withY <- Match(Y = foo$nowtot, Tr = foo$hasgirls, X = cbind(foo$Dems, foo$Repubs, foo$Christian, foo$age, foo$srvlng, foo$demvote), Weight.matrix = genout)
mb_withY <- MatchBalance(hasgirls ~ Dems + Repubs + Christian + age + srvlng + demvote, data = foo, match.out = mout_withY)##
## ***** (V1) Dems *****
## Before Matching After Matching
## mean treatment........ 0.45833 0.45833
## mean control.......... 0.50847 0.46154
## std mean diff......... -10.047 -0.64223
##
## mean raw eQQ diff..... 0.050847 0.0032051
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 1
##
## mean eCDF diff........ 0.025071 0.0016026
## med eCDF diff........ 0.025071 0.0016026
## max eCDF diff........ 0.050141 0.0032051
##
## var ratio (Tr/Co)..... 0.98809 0.99897
## T-test p-value........ 0.35571 0.31731
##
##
## ***** (V2) Repubs *****
## Before Matching After Matching
## mean treatment........ 0.53846 0.53846
## mean control.......... 0.49153 0.53846
## std mean diff......... 9.4 0
##
## mean raw eQQ diff..... 0.042373 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 0
##
## mean eCDF diff........ 0.023468 0
## med eCDF diff........ 0.023468 0
## max eCDF diff........ 0.046936 0
##
## var ratio (Tr/Co)..... 0.98911 1
## T-test p-value........ 0.3873 1
##
##
## ***** (V3) Christian *****
## Before Matching After Matching
## mean treatment........ 0.9391 0.9391
## mean control.......... 0.94915 0.9391
## std mean diff......... -4.1958 0
##
## mean raw eQQ diff..... 0.016949 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 0
##
## mean eCDF diff........ 0.005025 0
## med eCDF diff........ 0.005025 0
## max eCDF diff........ 0.01005 0
##
## var ratio (Tr/Co)..... 1.1787 1
## T-test p-value........ 0.68107 1
##
##
## ***** (V4) age *****
## Before Matching After Matching
## mean treatment........ 52.628 52.628
## mean control.......... 49.178 52.506
## std mean diff......... 38.385 1.355
##
## mean raw eQQ diff..... 3.661 0.58974
## med raw eQQ diff..... 4 1
## max raw eQQ diff..... 7 4
##
## mean eCDF diff........ 0.075348 0.012821
## med eCDF diff........ 0.075538 0.0064103
## max eCDF diff........ 0.17807 0.044872
##
## var ratio (Tr/Co)..... 0.71552 1.0074
## T-test p-value........ 0.0020402 0.43069
## KS Bootstrap p-value.. 0.002 0.786
## KS Naive p-value...... 0.0087659 0.91194
## KS Statistic.......... 0.17807 0.044872
##
##
## ***** (V5) srvlng *****
## Before Matching After Matching
## mean treatment........ 8.5865 8.5865
## mean control.......... 8.7458 8.7179
## std mean diff......... -2.1085 -1.7401
##
## mean raw eQQ diff..... 0.66949 0.45192
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 5 9
##
## mean eCDF diff........ 0.017181 0.012204
## med eCDF diff........ 0.01445 0.0080128
## max eCDF diff........ 0.051608 0.044872
##
## var ratio (Tr/Co)..... 0.77347 0.94705
## T-test p-value........ 0.85956 0.42669
## KS Bootstrap p-value.. 0.78 0.68
## KS Naive p-value...... 0.97653 0.91194
## KS Statistic.......... 0.051608 0.044872
##
##
## ***** (V6) demvote *****
## Before Matching After Matching
## mean treatment........ 0.49929 0.49929
## mean control.......... 0.50602 0.49811
## std mean diff......... -5.2747 0.93056
##
## mean raw eQQ diff..... 0.011441 0.010032
## med raw eQQ diff..... 0.01 0.01
## max raw eQQ diff..... 0.08 0.08
##
## mean eCDF diff........ 0.015928 0.015161
## med eCDF diff........ 0.010811 0.0096154
## max eCDF diff........ 0.048512 0.044872
##
## var ratio (Tr/Co)..... 1.1269 1.1572
## T-test p-value........ 0.61103 0.63267
## KS Bootstrap p-value.. 0.92 0.792
## KS Naive p-value...... 0.98776 0.91194
## KS Statistic.......... 0.048512 0.044872
##
##
## Before Matching Minimum p.value: 0.002
## Variable Name(s): age Number(s): 4
##
## After Matching Minimum p.value: 0.31731
## Variable Name(s): Dems Number(s): 1summary(mout_withY)##
## Estimate... 1.0737
## AI SE...... 2.213
## T-stat..... 0.48519
## p.val...... 0.62754
##
## Original number of observations.............. 430
## Original number of treated obs............... 312
## Matched number of observations............... 312
## Matched number of observations (unweighted). 312lower_bound = mout_withY$est - 1.96 * mout_withY$se
upper_bound = mout_withY$est + 1.96 * mout_withY$se
cat("The 95% confidence interval of the average treatment effect is [", lower_bound, "," ,upper_bound, "]")## The 95% confidence interval of the average treatment effect is [ -3.26369 , 5.411126 ]PART B:
foo <- read.csv(url("https://course-resources.minerva.kgi.edu/uploaded_files/mke/00089202-1711/daughters.csv"))
foo_treat <- subset(foo, foo$ngirls >= 2 & foo$nboys == 0)
foo_control <- subset(foo, foo$nboys >= 2 & foo$ngirls == 0)
foo2 <- rbind(foo_treat, foo_control)
dim(foo2)## [1] 91 83model2 <- lm(nowtot ~ Dems + Repubs + Christian + age + srvlng + demvote + hasgirls, data = foo2)
summary(model2)##
## Call:
## lm(formula = nowtot ~ Dems + Repubs + Christian + age + srvlng +
## demvote + hasgirls, data = foo2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.636 -6.608 0.928 8.992 46.235
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -18.4332 16.3697 -1.126 0.26335
## Dems 49.9284 4.4002 11.347 < 2e-16 ***
## Repubs NA NA NA NA
## Christian -3.4303 7.7211 -0.444 0.65798
## age -0.2558 0.2180 -1.173 0.24395
## srvlng 0.3908 0.2654 1.473 0.14461
## demvote 86.6631 17.9813 4.820 6.32e-06 ***
## hasgirls 12.2925 3.5008 3.511 0.00072 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.82 on 84 degrees of freedom
## Multiple R-squared: 0.839, Adjusted R-squared: 0.8275
## F-statistic: 72.97 on 6 and 84 DF, p-value: < 2.2e-16confint(model2)## 2.5 % 97.5 %
## (Intercept) -50.9860577 14.1196514
## Dems 41.1780715 58.6786775
## Repubs NA NA
## Christian -18.7845865 11.9239371
## age -0.6891980 0.1776850
## srvlng -0.1369568 0.9185161
## demvote 50.9052404 122.4209484
## hasgirls 5.3308756 19.2542092set.seed(2324)
genout2 <- GenMatch(Tr = foo2$hasgirls, X = cbind(foo2$Dems, foo2$Repubs, foo2$Christian, foo2$age, foo2$srvlng, foo2$demvote), pop.size = 20, nboots = 250)##
##
## Sun Apr 10 22:41:29 2022
## Domains:
## 0.000000e+00 <= X1 <= 1.000000e+03
## 0.000000e+00 <= X2 <= 1.000000e+03
## 0.000000e+00 <= X3 <= 1.000000e+03
## 0.000000e+00 <= X4 <= 1.000000e+03
## 0.000000e+00 <= X5 <= 1.000000e+03
## 0.000000e+00 <= X6 <= 1.000000e+03
##
## Data Type: Floating Point
## Operators (code number, name, population)
## (1) Cloning........................... 5
## (2) Uniform Mutation.................. 2
## (3) Boundary Mutation................. 2
## (4) Non-Uniform Mutation.............. 2
## (5) Polytope Crossover................ 2
## (6) Simple Crossover.................. 2
## (7) Whole Non-Uniform Mutation........ 2
## (8) Heuristic Crossover............... 2
## (9) Local-Minimum Crossover........... 0
##
## SOFT Maximum Number of Generations: 100
## Maximum Nonchanging Generations: 4
## Population size : 20
## Convergence Tolerance: 1.000000e-03
##
## Not Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.
## Not Checking Gradients before Stopping.
## Using Out of Bounds Individuals.
##
## Maximization Problem.
## GENERATION: 0 (initializing the population)
## Lexical Fit..... 4.424600e-01 6.003469e-01 6.696286e-01 7.120000e-01 9.840000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 20, #Total UniqueCount: 20
## var 1:
## best............ 9.293475e+02
## mean............ 4.681714e+02
## variance........ 8.152584e+04
## var 2:
## best............ 3.084540e+02
## mean............ 5.271953e+02
## variance........ 9.003735e+04
## var 3:
## best............ 8.001194e+02
## mean............ 3.349127e+02
## variance........ 8.501048e+04
## var 4:
## best............ 1.709408e+02
## mean............ 4.497491e+02
## variance........ 8.534085e+04
## var 5:
## best............ 2.916808e+02
## mean............ 4.406555e+02
## variance........ 8.114577e+04
## var 6:
## best............ 5.034455e+02
## mean............ 3.835392e+02
## variance........ 7.908836e+04
##
## GENERATION: 1
## Lexical Fit..... 4.424600e-01 6.003469e-01 6.696286e-01 7.120000e-01 9.920000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 13, #Total UniqueCount: 33
## var 1:
## best............ 1.937411e+02
## mean............ 6.243291e+02
## variance........ 9.769876e+04
## var 2:
## best............ 3.084540e+02
## mean............ 4.500741e+02
## variance........ 5.444226e+04
## var 3:
## best............ 8.001194e+02
## mean............ 5.401662e+02
## variance........ 1.043792e+05
## var 4:
## best............ 1.709408e+02
## mean............ 1.658333e+02
## variance........ 7.302264e+03
## var 5:
## best............ 2.916808e+02
## mean............ 4.660140e+02
## variance........ 6.773521e+04
## var 6:
## best............ 5.034455e+02
## mean............ 3.992819e+02
## variance........ 4.286519e+04
##
## GENERATION: 2
## Lexical Fit..... 4.424600e-01 6.003469e-01 6.696286e-01 7.400000e-01 9.960000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 45
## var 1:
## best............ 9.365884e+02
## mean............ 5.927958e+02
## variance........ 1.278823e+05
## var 2:
## best............ 3.084540e+02
## mean............ 3.074677e+02
## variance........ 3.938078e+03
## var 3:
## best............ 8.001194e+02
## mean............ 7.509834e+02
## variance........ 1.461658e+04
## var 4:
## best............ 1.709408e+02
## mean............ 2.079257e+02
## variance........ 1.258770e+04
## var 5:
## best............ 2.916808e+02
## mean............ 3.052627e+02
## variance........ 8.427622e+03
## var 6:
## best............ 5.034455e+02
## mean............ 5.398263e+02
## variance........ 7.949536e+03
##
## GENERATION: 3
## Lexical Fit..... 4.424600e-01 6.003469e-01 6.696286e-01 7.760000e-01 9.880000e-01 9.960000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 57
## var 1:
## best............ 1.022298e+02
## mean............ 5.311892e+02
## variance........ 1.266886e+05
## var 2:
## best............ 3.153794e+02
## mean............ 3.030091e+02
## variance........ 8.418810e+02
## var 3:
## best............ 8.726853e+02
## mean............ 7.910731e+02
## variance........ 7.639348e+03
## var 4:
## best............ 1.657895e+02
## mean............ 1.898282e+02
## variance........ 9.957129e+03
## var 5:
## best............ 2.936771e+02
## mean............ 2.958649e+02
## variance........ 2.917297e+03
## var 6:
## best............ 4.859064e+02
## mean............ 4.770910e+02
## variance........ 5.813069e+03
##
## GENERATION: 4
## Lexical Fit..... 4.424600e-01 6.003469e-01 6.696286e-01 7.760000e-01 9.880000e-01 9.960000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 69
## var 1:
## best............ 1.022298e+02
## mean............ 5.613359e+02
## variance........ 1.072145e+05
## var 2:
## best............ 3.153794e+02
## mean............ 3.099964e+02
## variance........ 8.357769e+02
## var 3:
## best............ 8.726853e+02
## mean............ 6.988867e+02
## variance........ 2.992970e+04
## var 4:
## best............ 1.657895e+02
## mean............ 1.633236e+02
## variance........ 2.791922e+02
## var 5:
## best............ 2.936771e+02
## mean............ 2.859652e+02
## variance........ 1.627083e+03
## var 6:
## best............ 4.859064e+02
## mean............ 4.891927e+02
## variance........ 7.368361e+03
##
## GENERATION: 5
## Lexical Fit..... 4.424600e-01 6.003469e-01 6.696286e-01 7.760000e-01 9.880000e-01 9.960000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 10, #Total UniqueCount: 79
## var 1:
## best............ 1.022298e+02
## mean............ 4.014125e+02
## variance........ 9.979589e+04
## var 2:
## best............ 3.153794e+02
## mean............ 3.275115e+02
## variance........ 1.710163e+03
## var 3:
## best............ 8.726853e+02
## mean............ 7.724323e+02
## variance........ 1.909199e+04
## var 4:
## best............ 1.657895e+02
## mean............ 1.822417e+02
## variance........ 5.189189e+03
## var 5:
## best............ 2.936771e+02
## mean............ 3.383906e+02
## variance........ 1.270683e+04
## var 6:
## best............ 4.859064e+02
## mean............ 5.064322e+02
## variance........ 3.202219e+03
##
## GENERATION: 6
## Lexical Fit..... 4.424600e-01 6.003469e-01 6.696286e-01 7.760000e-01 9.880000e-01 9.960000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 91
## var 1:
## best............ 1.022298e+02
## mean............ 4.403256e+02
## variance........ 8.049661e+04
## var 2:
## best............ 3.153794e+02
## mean............ 2.991970e+02
## variance........ 6.228253e+03
## var 3:
## best............ 8.726853e+02
## mean............ 7.595408e+02
## variance........ 2.162571e+04
## var 4:
## best............ 1.657895e+02
## mean............ 2.111712e+02
## variance........ 1.627123e+04
## var 5:
## best............ 2.936771e+02
## mean............ 3.155704e+02
## variance........ 7.024166e+03
## var 6:
## best............ 4.859064e+02
## mean............ 4.850533e+02
## variance........ 8.431503e+02
##
## GENERATION: 7
## Lexical Fit..... 4.424600e-01 6.003469e-01 6.696286e-01 8.040000e-01 9.880000e-01 9.960000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 9, #Total UniqueCount: 100
## var 1:
## best............ 8.900951e+01
## mean............ 3.410521e+02
## variance........ 1.049331e+05
## var 2:
## best............ 3.156828e+02
## mean............ 3.093487e+02
## variance........ 6.342108e+02
## var 3:
## best............ 8.758643e+02
## mean............ 7.599383e+02
## variance........ 2.876365e+04
## var 4:
## best............ 1.655638e+02
## mean............ 2.120631e+02
## variance........ 1.934225e+04
## var 5:
## best............ 2.937645e+02
## mean............ 2.908498e+02
## variance........ 3.801237e+03
## var 6:
## best............ 4.851331e+02
## mean............ 4.867729e+02
## variance........ 3.436151e+02
##
## GENERATION: 8
## Lexical Fit..... 4.424600e-01 6.003469e-01 6.696286e-01 8.040000e-01 9.880000e-01 9.960000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 112
## var 1:
## best............ 8.900951e+01
## mean............ 2.117857e+02
## variance........ 4.569035e+04
## var 2:
## best............ 3.156828e+02
## mean............ 3.270372e+02
## variance........ 1.467804e+04
## var 3:
## best............ 8.758643e+02
## mean............ 8.235576e+02
## variance........ 1.357938e+04
## var 4:
## best............ 1.655638e+02
## mean............ 2.182178e+02
## variance........ 2.732044e+04
## var 5:
## best............ 2.937645e+02
## mean............ 3.077500e+02
## variance........ 7.599008e+03
## var 6:
## best............ 4.851331e+02
## mean............ 4.961829e+02
## variance........ 3.561675e+03
##
## GENERATION: 9
## Lexical Fit..... 4.424600e-01 6.003469e-01 6.696286e-01 8.040000e-01 9.880000e-01 9.960000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 10, #Total UniqueCount: 122
## var 1:
## best............ 8.900951e+01
## mean............ 2.232582e+02
## variance........ 4.717121e+04
## var 2:
## best............ 3.156828e+02
## mean............ 3.105519e+02
## variance........ 3.700904e+02
## var 3:
## best............ 8.758643e+02
## mean............ 8.261784e+02
## variance........ 6.839198e+03
## var 4:
## best............ 1.655638e+02
## mean............ 1.706934e+02
## variance........ 4.289453e+02
## var 5:
## best............ 2.937645e+02
## mean............ 2.995581e+02
## variance........ 1.551937e+03
## var 6:
## best............ 4.851331e+02
## mean............ 4.982248e+02
## variance........ 5.578810e+03
##
## GENERATION: 10
## Lexical Fit..... 4.424600e-01 6.003469e-01 6.696286e-01 8.040000e-01 9.880000e-01 9.960000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 10, #Total UniqueCount: 132
## var 1:
## best............ 8.900951e+01
## mean............ 1.574849e+02
## variance........ 1.695707e+04
## var 2:
## best............ 3.156828e+02
## mean............ 3.229368e+02
## variance........ 5.796004e+02
## var 3:
## best............ 8.758643e+02
## mean............ 8.354259e+02
## variance........ 1.383487e+04
## var 4:
## best............ 1.655638e+02
## mean............ 1.669603e+02
## variance........ 1.706226e+01
## var 5:
## best............ 2.937645e+02
## mean............ 2.888274e+02
## variance........ 1.991678e+02
## var 6:
## best............ 4.851331e+02
## mean............ 5.136470e+02
## variance........ 5.530299e+03
##
## GENERATION: 11
## Lexical Fit..... 4.424600e-01 6.003469e-01 6.696286e-01 8.040000e-01 9.880000e-01 9.960000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 144
## var 1:
## best............ 8.900951e+01
## mean............ 1.214857e+02
## variance........ 6.304872e+03
## var 2:
## best............ 3.156828e+02
## mean............ 3.118972e+02
## variance........ 5.700417e+02
## var 3:
## best............ 8.758643e+02
## mean............ 8.456182e+02
## variance........ 1.235778e+04
## var 4:
## best............ 1.655638e+02
## mean............ 2.048345e+02
## variance........ 2.933928e+04
## var 5:
## best............ 2.937645e+02
## mean............ 2.849486e+02
## variance........ 1.432021e+03
## var 6:
## best............ 4.851331e+02
## mean............ 4.872592e+02
## variance........ 5.574477e+02
##
## GENERATION: 12
## Lexical Fit..... 4.424600e-01 6.003469e-01 6.696286e-01 8.040000e-01 9.880000e-01 9.960000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 8, #Total UniqueCount: 152
## var 1:
## best............ 8.900951e+01
## mean............ 1.015286e+02
## variance........ 1.958312e+03
## var 2:
## best............ 3.156828e+02
## mean............ 3.179348e+02
## variance........ 2.060566e+03
## var 3:
## best............ 8.758643e+02
## mean............ 8.373383e+02
## variance........ 1.843205e+04
## var 4:
## best............ 1.655638e+02
## mean............ 1.739300e+02
## variance........ 4.487269e+02
## var 5:
## best............ 2.937645e+02
## mean............ 3.195734e+02
## variance........ 5.129672e+03
## var 6:
## best............ 4.851331e+02
## mean............ 4.684641e+02
## variance........ 1.993905e+03
##
## 'wait.generations' limit reached.
## No significant improvement in 4 generations.
##
## Solution Lexical Fitness Value:
## 4.424600e-01 6.003469e-01 6.696286e-01 8.040000e-01 9.880000e-01 9.960000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
##
## Parameters at the Solution:
##
## X[ 1] : 8.900951e+01
## X[ 2] : 3.156828e+02
## X[ 3] : 8.758643e+02
## X[ 4] : 1.655638e+02
## X[ 5] : 2.937645e+02
## X[ 6] : 4.851331e+02
##
## Solution Found Generation 7
## Number of Generations Run 12
##
## Sun Apr 10 22:41:46 2022
## Total run time : 0 hours 0 minutes and 17 secondsmout2 <- Match(Y=foo2$nowtot, Tr = foo2$hasgirls, X = cbind(foo2$Dems, foo2$Repubs, foo2$Christian, foo2$age, foo2$srvlng, foo2$demvote), Weight.matrix = genout2)
mb_after2 <- MatchBalance(hasgirls ~ Dems + Repubs + Christian + age + srvlng + demvote, data = foo2, match.out = mout2)##
## ***** (V1) Dems *****
## Before Matching After Matching
## mean treatment........ 0.61702 0.61702
## mean control.......... 0.40909 0.61702
## std mean diff......... 42.317 0
##
## mean raw eQQ diff..... 0.20455 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 0
##
## mean eCDF diff........ 0.10397 0
## med eCDF diff........ 0.10397 0
## max eCDF diff........ 0.20793 0
##
## var ratio (Tr/Co)..... 0.97609 1
## T-test p-value........ 0.04806 1
##
##
## ***** (V2) Repubs *****
## Before Matching After Matching
## mean treatment........ 0.38298 0.38298
## mean control.......... 0.59091 0.38298
## std mean diff......... -42.317 0
##
## mean raw eQQ diff..... 0.22727 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 0
##
## mean eCDF diff........ 0.10397 0
## med eCDF diff........ 0.10397 0
## max eCDF diff........ 0.20793 0
##
## var ratio (Tr/Co)..... 0.97609 1
## T-test p-value........ 0.04806 1
##
##
## ***** (V3) Christian *****
## Before Matching After Matching
## mean treatment........ 0.91489 0.91489
## mean control.......... 0.97727 0.91489
## std mean diff......... -22.116 0
##
## mean raw eQQ diff..... 0.068182 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 0
##
## mean eCDF diff........ 0.03119 0
## med eCDF diff........ 0.03119 0
## max eCDF diff........ 0.062379 0
##
## var ratio (Tr/Co)..... 3.5005 1
## T-test p-value........ 0.1887 1
##
##
## ***** (V4) age *****
## Before Matching After Matching
## mean treatment........ 51.213 51.213
## mean control.......... 51.977 51.745
## std mean diff......... -8.2171 -5.7171
##
## mean raw eQQ diff..... 1.5682 0.95745
## med raw eQQ diff..... 1 1
## max raw eQQ diff..... 6 3
##
## mean eCDF diff........ 0.0288 0.02695
## med eCDF diff........ 0.025629 0.021277
## max eCDF diff........ 0.07882 0.06383
##
## var ratio (Tr/Co)..... 0.79756 1.0089
## T-test p-value........ 0.71354 0.60035
## KS Bootstrap p-value.. 0.976 0.998
## KS Naive p-value...... 0.99893 0.99998
## KS Statistic.......... 0.07882 0.06383
##
##
## ***** (V5) srvlng *****
## Before Matching After Matching
## mean treatment........ 7.9574 7.9574
## mean control.......... 10.568 8.1489
## std mean diff......... -38.919 -2.8546
##
## mean raw eQQ diff..... 2.8636 0.61702
## med raw eQQ diff..... 2 0
## max raw eQQ diff..... 14 2
##
## mean eCDF diff........ 0.079008 0.022695
## med eCDF diff........ 0.091876 0.021277
## max eCDF diff........ 0.13926 0.06383
##
## var ratio (Tr/Co)..... 0.48803 0.92491
## T-test p-value........ 0.13925 0.66963
## KS Bootstrap p-value.. 0.5 0.982
## KS Naive p-value...... 0.77019 0.99998
## KS Statistic.......... 0.13926 0.06383
##
##
## ***** (V6) demvote *****
## Before Matching After Matching
## mean treatment........ 0.51745 0.51745
## mean control.......... 0.48727 0.51
## std mean diff......... 24.982 6.1654
##
## mean raw eQQ diff..... 0.045909 0.025745
## med raw eQQ diff..... 0.045 0.02
## max raw eQQ diff..... 0.11 0.07
##
## mean eCDF diff........ 0.10233 0.053495
## med eCDF diff........ 0.091393 0.042553
## max eCDF diff........ 0.25193 0.12766
##
## var ratio (Tr/Co)..... 1.0419 1.3854
## T-test p-value........ 0.23199 0.44246
## KS Bootstrap p-value.. 0.072 0.728
## KS Naive p-value...... 0.11171 0.83838
## KS Statistic.......... 0.25193 0.12766
##
##
## Before Matching Minimum p.value: 0.04806
## Variable Name(s): Dems Repubs Number(s): 1 2
##
## After Matching Minimum p.value: 0.44246
## Variable Name(s): demvote Number(s): 6summary(mout2)##
## Estimate... 11.383
## AI SE...... 4.0247
## T-stat..... 2.8283
## p.val...... 0.0046797
##
## Original number of observations.............. 91
## Original number of treated obs............... 47
## Matched number of observations............... 47
## Matched number of observations (unweighted). 47lower_bound2 = mout2$est - 1.96 * mout2$se
upper_bound2 = mout2$est + 1.96 * mout2$se
cat("The 95% confidence interval of the average treatment effect is [", lower_bound2, "," ,upper_bound2, "]")## The 95% confidence interval of the average treatment effect is [ 3.494597 , 19.27136 ]genout2_specified <- GenMatch(Tr = foo2$hasgirls, X = cbind(foo2$Dems, foo2$Repubs, foo2$Christian, foo2$age, foo2$srvlng, foo2$demvote, foo2$white, foo2$female), pop.size = 20, nboots = 250)##
##
## Sun Apr 10 22:41:47 2022
## Domains:
## 0.000000e+00 <= X1 <= 1.000000e+03
## 0.000000e+00 <= X2 <= 1.000000e+03
## 0.000000e+00 <= X3 <= 1.000000e+03
## 0.000000e+00 <= X4 <= 1.000000e+03
## 0.000000e+00 <= X5 <= 1.000000e+03
## 0.000000e+00 <= X6 <= 1.000000e+03
## 0.000000e+00 <= X7 <= 1.000000e+03
## 0.000000e+00 <= X8 <= 1.000000e+03
##
## Data Type: Floating Point
## Operators (code number, name, population)
## (1) Cloning........................... 5
## (2) Uniform Mutation.................. 2
## (3) Boundary Mutation................. 2
## (4) Non-Uniform Mutation.............. 2
## (5) Polytope Crossover................ 2
## (6) Simple Crossover.................. 2
## (7) Whole Non-Uniform Mutation........ 2
## (8) Heuristic Crossover............... 2
## (9) Local-Minimum Crossover........... 0
##
## SOFT Maximum Number of Generations: 100
## Maximum Nonchanging Generations: 4
## Population size : 20
## Convergence Tolerance: 1.000000e-03
##
## Not Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.
## Not Checking Gradients before Stopping.
## Using Out of Bounds Individuals.
##
## Maximization Problem.
## GENERATION: 0 (initializing the population)
## Lexical Fit..... 8.001714e-02 8.001714e-02 8.001714e-02 8.001714e-02 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.294060e-01 3.731547e-01 3.943391e-01 4.240000e-01 8.760000e-01 9.880000e-01 1.000000e+00 1.000000e+00
## #unique......... 20, #Total UniqueCount: 20
## var 1:
## best............ 3.404333e+02
## mean............ 4.384249e+02
## variance........ 1.179087e+05
## var 2:
## best............ 2.383439e+02
## mean............ 4.321657e+02
## variance........ 5.968209e+04
## var 3:
## best............ 2.383137e+02
## mean............ 4.394400e+02
## variance........ 7.170056e+04
## var 4:
## best............ 8.322159e+02
## mean............ 5.727523e+02
## variance........ 9.188758e+04
## var 5:
## best............ 1.684170e+02
## mean............ 4.362754e+02
## variance........ 7.067681e+04
## var 6:
## best............ 9.984728e+02
## mean............ 5.823259e+02
## variance........ 1.100798e+05
## var 7:
## best............ 6.432509e+02
## mean............ 4.040143e+02
## variance........ 9.628071e+04
## var 8:
## best............ 4.149353e+02
## mean............ 5.309907e+02
## variance........ 1.069360e+05
##
## GENERATION: 1
## Lexical Fit..... 1.551525e-01 1.551525e-01 1.551525e-01 1.551525e-01 1.551525e-01 1.551525e-01 1.839089e-01 2.727392e-01 3.160000e-01 7.153110e-01 8.560000e-01 9.840000e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 13, #Total UniqueCount: 33
## var 1:
## best............ 3.400124e+02
## mean............ 2.955884e+02
## variance........ 8.553916e+04
## var 2:
## best............ 3.335243e+02
## mean............ 3.871914e+02
## variance........ 2.604512e+04
## var 3:
## best............ 3.389693e+02
## mean............ 5.622344e+02
## variance........ 8.361320e+04
## var 4:
## best............ 7.090632e+02
## mean............ 4.844830e+02
## variance........ 1.056967e+05
## var 5:
## best............ 1.684510e+02
## mean............ 3.782347e+02
## variance........ 4.895480e+04
## var 6:
## best............ 9.988146e+02
## mean............ 8.737712e+02
## variance........ 1.663889e+04
## var 7:
## best............ 6.709617e+02
## mean............ 3.360079e+02
## variance........ 6.483780e+04
## var 8:
## best............ 3.399098e+02
## mean............ 4.263291e+02
## variance........ 6.541904e+04
##
## GENERATION: 2
## Lexical Fit..... 1.551525e-01 1.551525e-01 1.551525e-01 1.551525e-01 1.551525e-01 1.551525e-01 3.035008e-01 3.173944e-01 3.173944e-01 4.214416e-01 4.240000e-01 5.353141e-01 8.800000e-01 9.840000e-01 1.000000e+00 1.000000e+00
## #unique......... 13, #Total UniqueCount: 46
## var 1:
## best............ 3.400124e+02
## mean............ 2.360358e+02
## variance........ 2.770059e+04
## var 2:
## best............ 3.335243e+02
## mean............ 3.637361e+02
## variance........ 1.737818e+04
## var 3:
## best............ 3.248698e+02
## mean............ 4.924060e+02
## variance........ 7.747380e+04
## var 4:
## best............ 7.090632e+02
## mean............ 6.373209e+02
## variance........ 7.309747e+04
## var 5:
## best............ 1.684510e+02
## mean............ 2.893330e+02
## variance........ 2.916304e+04
## var 6:
## best............ 9.988146e+02
## mean............ 8.751428e+02
## variance........ 2.700602e+04
## var 7:
## best............ 6.709617e+02
## mean............ 3.665602e+02
## variance........ 7.281166e+04
## var 8:
## best............ 3.399098e+02
## mean............ 2.720737e+02
## variance........ 1.068027e+04
##
## GENERATION: 3
## Lexical Fit..... 1.551525e-01 1.551525e-01 1.551525e-01 1.551525e-01 1.551525e-01 1.551525e-01 3.173944e-01 3.173944e-01 3.440000e-01 3.469493e-01 4.075674e-01 5.450929e-01 9.640000e-01 9.920000e-01 1.000000e+00 1.000000e+00
## #unique......... 12, #Total UniqueCount: 58
## var 1:
## best............ 3.400124e+02
## mean............ 2.731983e+02
## variance........ 2.015687e+04
## var 2:
## best............ 3.335243e+02
## mean............ 3.362687e+02
## variance........ 1.630328e+04
## var 3:
## best............ 3.248698e+02
## mean............ 4.354872e+02
## variance........ 5.333918e+04
## var 4:
## best............ 8.051509e+02
## mean............ 6.467903e+02
## variance........ 6.565415e+04
## var 5:
## best............ 1.684510e+02
## mean............ 2.425129e+02
## variance........ 9.883639e+03
## var 6:
## best............ 9.988146e+02
## mean............ 9.076434e+02
## variance........ 1.900512e+04
## var 7:
## best............ 6.829108e+02
## mean............ 4.143338e+02
## variance........ 7.342387e+04
## var 8:
## best............ 3.399098e+02
## mean............ 2.565420e+02
## variance........ 6.680133e+03
##
## GENERATION: 4
## Lexical Fit..... 1.551525e-01 1.551525e-01 1.551525e-01 1.551525e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.189180e-01 3.635497e-01 4.240000e-01 5.825066e-01 6.960000e-01 9.800000e-01
## #unique......... 13, #Total UniqueCount: 71
## var 1:
## best............ 3.411394e+02
## mean............ 3.359093e+02
## variance........ 7.511352e+03
## var 2:
## best............ 3.719294e+02
## mean............ 3.559707e+02
## variance........ 3.775513e+03
## var 3:
## best............ 3.209349e+02
## mean............ 3.453297e+02
## variance........ 2.052429e+04
## var 4:
## best............ 7.112545e+02
## mean............ 6.744251e+02
## variance........ 4.069810e+04
## var 5:
## best............ 1.676127e+02
## mean............ 2.027451e+02
## variance........ 7.244913e+03
## var 6:
## best............ 9.992147e+02
## mean............ 9.721626e+02
## variance........ 3.133559e+03
## var 7:
## best............ 2.782766e+02
## mean............ 6.264445e+02
## variance........ 2.930228e+04
## var 8:
## best............ 3.407796e+02
## mean............ 3.132111e+02
## variance........ 3.403396e+03
##
## GENERATION: 5
## Lexical Fit..... 1.551525e-01 1.551525e-01 1.551525e-01 1.551525e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.189180e-01 3.635497e-01 4.240000e-01 5.825066e-01 6.960000e-01 9.800000e-01
## #unique......... 12, #Total UniqueCount: 83
## var 1:
## best............ 3.411394e+02
## mean............ 3.440945e+02
## variance........ 2.827798e+02
## var 2:
## best............ 3.719294e+02
## mean............ 3.796959e+02
## variance........ 1.102326e+04
## var 3:
## best............ 3.209349e+02
## mean............ 3.246799e+02
## variance........ 2.791641e+02
## var 4:
## best............ 7.112545e+02
## mean............ 7.704905e+02
## variance........ 4.590561e+03
## var 5:
## best............ 1.676127e+02
## mean............ 2.194748e+02
## variance........ 3.186651e+04
## var 6:
## best............ 9.992147e+02
## mean............ 9.824489e+02
## variance........ 2.620841e+03
## var 7:
## best............ 2.782766e+02
## mean............ 5.378111e+02
## variance........ 3.480881e+04
## var 8:
## best............ 3.407796e+02
## mean............ 3.570306e+02
## variance........ 3.547965e+03
##
## GENERATION: 6
## Lexical Fit..... 1.551525e-01 1.551525e-01 1.551525e-01 1.551525e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.189180e-01 3.635497e-01 4.240000e-01 5.825066e-01 6.960000e-01 9.800000e-01
## #unique......... 12, #Total UniqueCount: 95
## var 1:
## best............ 3.411394e+02
## mean............ 3.460625e+02
## variance........ 6.225608e+02
## var 2:
## best............ 3.719294e+02
## mean............ 3.812686e+02
## variance........ 2.685304e+03
## var 3:
## best............ 3.209349e+02
## mean............ 3.245578e+02
## variance........ 2.423441e+03
## var 4:
## best............ 7.112545e+02
## mean............ 7.389566e+02
## variance........ 3.717534e+03
## var 5:
## best............ 1.676127e+02
## mean............ 1.918949e+02
## variance........ 1.012031e+04
## var 6:
## best............ 9.992147e+02
## mean............ 9.692823e+02
## variance........ 1.694379e+04
## var 7:
## best............ 2.782766e+02
## mean............ 3.710172e+02
## variance........ 2.587312e+04
## var 8:
## best............ 3.407796e+02
## mean............ 3.279993e+02
## variance........ 9.102650e+02
##
## GENERATION: 7
## Lexical Fit..... 1.551525e-01 1.551525e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 4.335631e-01 4.883156e-01 5.360000e-01 5.884981e-01 9.440000e-01 9.880000e-01
## #unique......... 10, #Total UniqueCount: 105
## var 1:
## best............ 3.411394e+02
## mean............ 3.349257e+02
## variance........ 7.361403e+02
## var 2:
## best............ 3.656871e+02
## mean............ 3.568628e+02
## variance........ 2.780548e+03
## var 3:
## best............ 3.207194e+02
## mean............ 3.222653e+02
## variance........ 3.160683e+02
## var 4:
## best............ 8.084412e+02
## mean............ 7.221408e+02
## variance........ 7.507271e+02
## var 5:
## best............ 1.676127e+02
## mean............ 1.714018e+02
## variance........ 1.876041e+03
## var 6:
## best............ 9.992147e+02
## mean............ 9.593840e+02
## variance........ 9.737514e+03
## var 7:
## best............ 2.782766e+02
## mean............ 2.648806e+02
## variance........ 4.578542e+03
## var 8:
## best............ 3.407796e+02
## mean............ 3.759387e+02
## variance........ 1.608831e+04
##
## GENERATION: 8
## Lexical Fit..... 1.551525e-01 1.551525e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 4.335631e-01 4.883156e-01 5.884981e-01 6.120000e-01 9.720000e-01 9.800000e-01
## #unique......... 9, #Total UniqueCount: 114
## var 1:
## best............ 3.411394e+02
## mean............ 3.407077e+02
## variance........ 2.893974e+00
## var 2:
## best............ 3.655379e+02
## mean............ 3.539316e+02
## variance........ 3.502875e+03
## var 3:
## best............ 3.207194e+02
## mean............ 3.274396e+02
## variance........ 5.367178e+02
## var 4:
## best............ 8.107650e+02
## mean............ 7.684776e+02
## variance........ 2.900374e+03
## var 5:
## best............ 1.676127e+02
## mean............ 1.648107e+02
## variance........ 3.116912e+02
## var 6:
## best............ 9.992147e+02
## mean............ 9.523565e+02
## variance........ 1.298205e+04
## var 7:
## best............ 2.782766e+02
## mean............ 2.755479e+02
## variance........ 5.264469e+02
## var 8:
## best............ 3.407796e+02
## mean............ 3.566451e+02
## variance........ 1.233406e+04
##
## GENERATION: 9
## Lexical Fit..... 1.551525e-01 1.551525e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 4.335631e-01 4.883156e-01 5.884981e-01 6.120000e-01 9.720000e-01 9.800000e-01
## #unique......... 10, #Total UniqueCount: 124
## var 1:
## best............ 3.411394e+02
## mean............ 3.406400e+02
## variance........ 2.418147e+02
## var 2:
## best............ 3.655379e+02
## mean............ 3.572470e+02
## variance........ 8.643318e+02
## var 3:
## best............ 3.207194e+02
## mean............ 3.398494e+02
## variance........ 2.347782e+03
## var 4:
## best............ 8.107650e+02
## mean............ 7.816013e+02
## variance........ 9.138240e+03
## var 5:
## best............ 1.676127e+02
## mean............ 1.846263e+02
## variance........ 5.036956e+03
## var 6:
## best............ 9.992147e+02
## mean............ 9.944405e+02
## variance........ 3.864342e+02
## var 7:
## best............ 2.782766e+02
## mean............ 2.733280e+02
## variance........ 1.362484e+03
## var 8:
## best............ 3.407796e+02
## mean............ 3.312350e+02
## variance........ 1.065757e+03
##
## GENERATION: 10
## Lexical Fit..... 1.551525e-01 1.551525e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 4.335631e-01 4.883156e-01 5.884981e-01 6.120000e-01 9.720000e-01 9.800000e-01
## #unique......... 9, #Total UniqueCount: 133
## var 1:
## best............ 3.411394e+02
## mean............ 3.409572e+02
## variance........ 6.310288e-01
## var 2:
## best............ 3.655379e+02
## mean............ 3.763863e+02
## variance........ 3.549725e+03
## var 3:
## best............ 3.207194e+02
## mean............ 3.221968e+02
## variance........ 1.700267e+03
## var 4:
## best............ 8.107650e+02
## mean............ 8.011368e+02
## variance........ 4.728346e+03
## var 5:
## best............ 1.676127e+02
## mean............ 1.692297e+02
## variance........ 7.107124e+01
## var 6:
## best............ 9.992147e+02
## mean............ 9.830352e+02
## variance........ 2.440247e+03
## var 7:
## best............ 2.782766e+02
## mean............ 2.859984e+02
## variance........ 2.067443e+03
## var 8:
## best............ 3.407796e+02
## mean............ 3.341201e+02
## variance........ 1.380574e+03
##
## GENERATION: 11
## Lexical Fit..... 1.551525e-01 1.551525e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 4.335631e-01 4.883156e-01 5.884981e-01 6.120000e-01 9.720000e-01 9.800000e-01
## #unique......... 8, #Total UniqueCount: 141
## var 1:
## best............ 3.411394e+02
## mean............ 3.504727e+02
## variance........ 3.334432e+03
## var 2:
## best............ 3.655379e+02
## mean............ 3.572870e+02
## variance........ 4.278696e+02
## var 3:
## best............ 3.207194e+02
## mean............ 3.203188e+02
## variance........ 1.444673e+00
## var 4:
## best............ 8.107650e+02
## mean............ 7.813132e+02
## variance........ 8.373785e+03
## var 5:
## best............ 1.676127e+02
## mean............ 2.113518e+02
## variance........ 2.043083e+04
## var 6:
## best............ 9.992147e+02
## mean............ 9.198271e+02
## variance........ 5.582340e+04
## var 7:
## best............ 2.782766e+02
## mean............ 2.799425e+02
## variance........ 1.799798e+02
## var 8:
## best............ 3.407796e+02
## mean............ 3.371795e+02
## variance........ 6.561627e+02
##
## GENERATION: 12
## Lexical Fit..... 1.551525e-01 1.551525e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 4.335631e-01 4.883156e-01 5.884981e-01 6.120000e-01 9.720000e-01 9.800000e-01
## #unique......... 6, #Total UniqueCount: 147
## var 1:
## best............ 3.411394e+02
## mean............ 3.844425e+02
## variance........ 1.456440e+04
## var 2:
## best............ 3.655379e+02
## mean............ 3.669064e+02
## variance........ 9.157810e+02
## var 3:
## best............ 3.207194e+02
## mean............ 3.190019e+02
## variance........ 1.490744e+02
## var 4:
## best............ 8.107650e+02
## mean............ 7.816105e+02
## variance........ 1.722824e+04
## var 5:
## best............ 1.676127e+02
## mean............ 1.900838e+02
## variance........ 7.605194e+03
## var 6:
## best............ 9.992147e+02
## mean............ 9.821590e+02
## variance........ 2.625971e+03
## var 7:
## best............ 2.782766e+02
## mean............ 3.006486e+02
## variance........ 5.463717e+03
## var 8:
## best............ 3.407796e+02
## mean............ 3.524409e+02
## variance........ 2.673145e+03
##
## GENERATION: 13
## Lexical Fit..... 1.551525e-01 1.551525e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 4.335631e-01 4.883156e-01 5.884981e-01 6.120000e-01 9.720000e-01 9.800000e-01
## #unique......... 6, #Total UniqueCount: 153
## var 1:
## best............ 3.411394e+02
## mean............ 3.371346e+02
## variance........ 7.705543e+02
## var 2:
## best............ 3.655379e+02
## mean............ 3.747403e+02
## variance........ 3.196401e+03
## var 3:
## best............ 3.207194e+02
## mean............ 3.094717e+02
## variance........ 2.798989e+03
## var 4:
## best............ 8.107650e+02
## mean............ 8.098853e+02
## variance........ 3.142103e+02
## var 5:
## best............ 1.676127e+02
## mean............ 1.785567e+02
## variance........ 1.585748e+03
## var 6:
## best............ 9.992147e+02
## mean............ 9.981903e+02
## variance........ 2.085646e+01
## var 7:
## best............ 2.782766e+02
## mean............ 3.016874e+02
## variance........ 5.537102e+03
## var 8:
## best............ 3.407796e+02
## mean............ 3.500699e+02
## variance........ 1.026714e+03
##
## 'wait.generations' limit reached.
## No significant improvement in 4 generations.
##
## Solution Lexical Fitness Value:
## 1.551525e-01 1.551525e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 3.173944e-01 4.335631e-01 4.883156e-01 5.884981e-01 6.120000e-01 9.720000e-01 9.800000e-01
##
## Parameters at the Solution:
##
## X[ 1] : 3.411394e+02
## X[ 2] : 3.655379e+02
## X[ 3] : 3.207194e+02
## X[ 4] : 8.107650e+02
## X[ 5] : 1.676127e+02
## X[ 6] : 9.992147e+02
## X[ 7] : 2.782766e+02
## X[ 8] : 3.407796e+02
##
## Solution Found Generation 8
## Number of Generations Run 13
##
## Sun Apr 10 22:42:00 2022
## Total run time : 0 hours 0 minutes and 13 secondsmout2_specified <- Match(Y=foo2$nowtot, Tr = foo2$hasgirls, X = cbind(foo2$Dems, foo2$Repubs, foo2$Christian, foo2$age, foo2$srvlng, foo2$demvote, foo2$white, foo2$female), Weight.matrix = genout2_specified)
mb_after2_specified <- MatchBalance(hasgirls ~ Dems + Repubs + Christian + age + srvlng + demvote + female + white, data = foo2, match.out = mout2_specified)##
## ***** (V1) Dems *****
## Before Matching After Matching
## mean treatment........ 0.61702 0.61702
## mean control.......... 0.40909 0.59574
## std mean diff......... 42.317 4.3301
##
## mean raw eQQ diff..... 0.20455 0.021277
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 1
##
## mean eCDF diff........ 0.10397 0.010638
## med eCDF diff........ 0.10397 0.010638
## max eCDF diff........ 0.20793 0.021277
##
## var ratio (Tr/Co)..... 0.97609 0.9812
## T-test p-value........ 0.04806 0.31739
##
##
## ***** (V2) Repubs *****
## Before Matching After Matching
## mean treatment........ 0.38298 0.38298
## mean control.......... 0.59091 0.40426
## std mean diff......... -42.317 -4.3301
##
## mean raw eQQ diff..... 0.22727 0.021277
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 1
##
## mean eCDF diff........ 0.10397 0.010638
## med eCDF diff........ 0.10397 0.010638
## max eCDF diff........ 0.20793 0.021277
##
## var ratio (Tr/Co)..... 0.97609 0.9812
## T-test p-value........ 0.04806 0.31739
##
##
## ***** (V3) Christian *****
## Before Matching After Matching
## mean treatment........ 0.91489 0.91489
## mean control.......... 0.97727 0.93617
## std mean diff......... -22.116 -7.5434
##
## mean raw eQQ diff..... 0.068182 0.021277
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 1
##
## mean eCDF diff........ 0.03119 0.010638
## med eCDF diff........ 0.03119 0.010638
## max eCDF diff........ 0.062379 0.021277
##
## var ratio (Tr/Co)..... 3.5005 1.303
## T-test p-value........ 0.1887 0.31739
##
##
## ***** (V4) age *****
## Before Matching After Matching
## mean treatment........ 51.213 51.213
## mean control.......... 51.977 51.66
## std mean diff......... -8.2171 -4.8024
##
## mean raw eQQ diff..... 1.5682 1.1702
## med raw eQQ diff..... 1 1
## max raw eQQ diff..... 6 4
##
## mean eCDF diff........ 0.0288 0.029255
## med eCDF diff........ 0.025629 0.021277
## max eCDF diff........ 0.07882 0.085106
##
## var ratio (Tr/Co)..... 0.79756 1.0439
## T-test p-value........ 0.71354 0.5885
## KS Bootstrap p-value.. 0.966 0.97
## KS Naive p-value...... 0.99893 0.99568
## KS Statistic.......... 0.07882 0.085106
##
##
## ***** (V5) srvlng *****
## Before Matching After Matching
## mean treatment........ 7.9574 7.9574
## mean control.......... 10.568 8.4255
## std mean diff......... -38.919 -6.9779
##
## mean raw eQQ diff..... 2.8636 0.76596
## med raw eQQ diff..... 2 0
## max raw eQQ diff..... 14 4
##
## mean eCDF diff........ 0.079008 0.023936
## med eCDF diff........ 0.091876 0.021277
## max eCDF diff........ 0.13926 0.06383
##
## var ratio (Tr/Co)..... 0.48803 0.77656
## T-test p-value........ 0.13925 0.48832
## KS Bootstrap p-value.. 0.502 0.992
## KS Naive p-value...... 0.77019 0.99998
## KS Statistic.......... 0.13926 0.06383
##
##
## ***** (V6) demvote *****
## Before Matching After Matching
## mean treatment........ 0.51745 0.51745
## mean control.......... 0.48727 0.50979
## std mean diff......... 24.982 6.3415
##
## mean raw eQQ diff..... 0.045909 0.028511
## med raw eQQ diff..... 0.045 0.03
## max raw eQQ diff..... 0.11 0.07
##
## mean eCDF diff........ 0.10233 0.058079
## med eCDF diff........ 0.091393 0.06383
## max eCDF diff........ 0.25193 0.14894
##
## var ratio (Tr/Co)..... 1.0419 1.1922
## T-test p-value........ 0.23199 0.43356
## KS Bootstrap p-value.. 0.082 0.546
## KS Naive p-value...... 0.11171 0.67438
## KS Statistic.......... 0.25193 0.14894
##
##
## ***** (V7) female *****
## Before Matching After Matching
## mean treatment........ 0.14894 0.14894
## mean control.......... 0.045455 0.10638
## std mean diff......... 28.755 11.824
##
## mean raw eQQ diff..... 0.090909 0.042553
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 1
##
## mean eCDF diff........ 0.051741 0.021277
## med eCDF diff........ 0.051741 0.021277
## max eCDF diff........ 0.10348 0.042553
##
## var ratio (Tr/Co)..... 2.9171 1.3333
## T-test p-value........ 0.095836 0.15515
##
##
## ***** (V8) white *****
## Before Matching After Matching
## mean treatment........ 0.89362 0.89362
## mean control.......... 0.90909 0.91489
## std mean diff......... -4.965 -6.8268
##
## mean raw eQQ diff..... 0.022727 0.021277
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 1
##
## mean eCDF diff........ 0.0077369 0.010638
## med eCDF diff........ 0.0077369 0.010638
## max eCDF diff........ 0.015474 0.021277
##
## var ratio (Tr/Co)..... 1.1486 1.2209
## T-test p-value........ 0.80701 0.31739
##
##
## Before Matching Minimum p.value: 0.04806
## Variable Name(s): Dems Repubs Number(s): 1 2
##
## After Matching Minimum p.value: 0.15515
## Variable Name(s): female Number(s): 7summary(mout2_specified)##
## Estimate... 15.638
## AI SE...... 4.8988
## T-stat..... 3.1923
## p.val...... 0.0014115
##
## Original number of observations.............. 91
## Original number of treated obs............... 47
## Matched number of observations............... 47
## Matched number of observations (unweighted). 47lower_bound2_specified = mout2_specified$est - 1.96 * mout2_specified$se
upper_bound2_specified = mout2_specified$est + 1.96 * mout2_specified$se
cat("The 95% confidence interval of the average treatment effect is [", lower_bound2_specified, "," ,upper_bound2_specified, "]")## The 95% confidence interval of the average treatment effect is [ 6.036683 , 25.23991 ]QUESTION 2
library(rgenoud)
library(date)
library(Matching)
rm(list=ls())
foo <- read.csv("Downloads/district - district.csv")
#dim(foo)
#76772 14
missing_bank_code <- rep(0, 76772)
missing_bank_name <- rep(0, 76772)
missing_date_of_birth <- rep(0, 76772)
NA_district_code <- rep(0, 76772)
NA_capital <- rep(0, 76772)
NA_credit_proposal <- rep(0, 76772)
foo <- cbind(foo, missing_bank_code,
missing_bank_name,
missing_date_of_birth,
NA_district_code,
NA_capital,
NA_credit_proposal)
foo$missing_bank_code[which(foo$bank_code == "")] <- 1
foo$missing_bank_name[which(foo$bank_name == "")] <- 1
foo$missing_date_of_birth[which(foo$date_of_birth == "")] <- 1
foo$NA_capital[which(is.na(foo$capital) == TRUE)] <- 1
foo$NA_credit_proposal[which(is.na(foo$credit_proposal) == TRUE)] <- 1
foo$NA_district_code[which(is.na(foo$district_code) == TRUE)] <- 1
#change the dates to R-readable format
foo$R_date_of_birth <- as.character(foo[,3])
for(i in 1:length(foo[,3])) {foo$R_date_of_birth[i] <- as.date(foo$R_date_of_birth[i], order =
"dmy")}
foo$R_date_of_birth <- as.date(as.numeric(foo$R_date_of_birth))
oldest <- which(foo$R_date_of_birth < as.date("1-Jan-1910"))
youngest <- which(foo$R_date_of_birth > as.date("1 Jan 2001"))
foo$oldest <- rep(0, length(foo[,3]))
foo$youngest <- rep(0, length(foo[,3]))
foo$outlier_ages <- rep(0, length(foo[,3]))
foo$oldest[oldest] <- 1
foo$youngest[youngest] <- 1
foo$outlier_ages[c(oldest,youngest)] <- 1
foo$R_date_of_birth[which(is.na(foo$R_date_of_birth) == TRUE)] <- -9999999
#standard deviation
SD <- 365/sd(foo$R_date_of_birth)
# 0.000305876
#this obs with specific postal code makes no sense
#foo <- foo[-which(foo$postal_code == 9151), ]
#to extract only the first 2 digits of district codes:
foo$district_code1 <- foo$district_code%/% 100
foo$district_code1[which(is.na(foo$district_code1) == TRUE)] <- -9999999
#credit_proposal feature engineering
foo$credit_proposal[which(is.na(foo$credit_proposal) == TRUE)] <- 9999999
foo$credit_proposal_0 <- foo$credit_proposal == 0 & (is.na(foo$credit_proposal) == FALSE)
foo$credit_proposal_0to5 <- foo$credit_proposal > 0 & foo$credit_proposal < 5000000 &
(is.na(foo$credit_proposal) == FALSE)
foo$credit_proposal_5to10 <- foo$credit_proposal >= 5000000 & foo$credit_proposal < 10000000 &
(is.na(foo$credit_proposal) == FALSE)
foo$credit_proposal_10to20 <- foo$credit_proposal >= 10000000 & foo$credit_proposal < 20000000 &
(is.na(foo$credit_proposal) == FALSE)
foo$credit_proposal_20up <- foo$credit_proposal >= 20000000 & (is.na(foo$credit_proposal) ==
FALSE)
foo$credit_proposal_transformed <-
1*foo$credit_proposal_0 +
2*foo$credit_proposal_0to5 +
3*foo$credit_proposal_5to10 +
4*foo$credit_proposal_10to20 +
5*foo$credit_proposal_20up +
6*foo$NA_credit_proposal
#NA capital
foo$capital[which(is.na(foo$capital) == TRUE)] <- 9999999
#capital feature engineering
foo$capital_0 <- foo$capital == 0 & (is.na(foo$capital) == FALSE)
foo$capital_0to2 <- foo$capital > 0 & foo$capital < 200000 & (is.na(foo$capital) == FALSE)
foo$capital_2to5 <- foo$capital >= 200000 & foo$capital < 500000 & (is.na(foo$capital) == FALSE)
foo$capital_5to10 <- foo$capital >= 500000 & foo$capital < 1000000 & (is.na(foo$capital) ==
FALSE)
foo$capital_10to20 <- foo$capital >= 1000000 & foo$capital < 2000000 & (is.na(foo$capital) ==
FALSE)
foo$capital_20to50 <- foo$capital >= 2000000 & foo$capital < 5000000 & (is.na(foo$capital) ==
FALSE)
foo$capital_50up <- foo$capital >= 5000000 & (is.na(foo$capital) == FALSE)
foo$capital_transformed <-
1*foo$capital_0 +
2*foo$capital_0to2 +
3*foo$capital_2to5 +
4*foo$capital_5to10 +
5*foo$capital_10to20 +
6*foo$capital_20to50 +
7*foo$capital_50up +
8*foo$NA_capital
#worker feature engineering
#remove outlier in the control group (10 million workers)
foo <- foo[-which(foo$worker == max(foo$worker)),]
foo$worker_0 <- foo$worker == 0
foo$worker_1 <- foo$worker == 1
foo$worker_2 <- foo$worker == 2
foo$worker_3 <- foo$worker == 3
foo$worker_4 <- foo$worker == 4
foo$worker_5to9 <- foo$worker >=5 & foo$worker < 10
foo$worker_10to24 <- foo$worker >=10 & foo$worker < 25
foo$worker_25to99 <- foo$worker >=25 & foo$worker < 100
foo$worker_100up <- foo$worker >= 100
foo$worker_transformed <-
1*foo$worker_0 +
2*foo$worker_1 +
3*foo$worker_2 +
4*foo$worker_3 +
5*foo$worker_4 +
6*foo$worker_5to9 +
7*foo$worker_10to24 +
8*foo$worker_25to99 +
9*foo$worker_100up
#treatment Indicator
foo$treat <- foo$status == "Sudah"
#the only bank code (var 1) is PEM
#the only bank name (var 2) is PEMDA -- regional government, not a bank
#(var 3) 174 outlier ages
#(var 4) gender is 3 levels: men, women, and business entity ("LAKI-LAKI" "PEREMPUAN" "BADAN USAHA")
#(var 5) marital_status: business agency, single, married ("BADAN USAHA" "BELUM KAWIN" "KAWIN")
###(var 6) EDUCATION
#"BADAN USAHA" "DIPLOMA" "LAINNYA" "SARJANA" "SD" "SMP" "SMU"
#"BUSINESS AGENCY" "DIPLOMA" "OTHER" "GRADUATE" "Elementary School" "Middle School" "High School"
###(var 7) OCCUPATION
#[1]"KARYAWAN SWASTA" "LAIN-LAIN/BADAN USAHA" "NELAYAN" "PEDAGANG"
#[5]"PENSIUNAN/PURNAWIRAWAN" "PETANI" "PNS" "PROFESIONAL"
#[9]"TNI/POLRI" "WIRASWASTA"
#[1]"PRIVATE EMPLOYEES" [2] "OTHERS / BUSINESS AGENCIES" [3] "FISHERMANS" [4] "TRADERS"
#[5]"PENSIONERS / PURNAWIRAWAN" [6] "FARMERS" [7] "CIVIL SERVANT" [8] "PROFESSIONAL"
#[9]"ARMY / POLICE" [10] "ENTREPRENEURS"
###(var 8) 938 unique POSTAL CODES
###(var 9) 63 unique DISTRICT CODES
###(var 10) : "worker". unknown meaning... 300+ unique numerical values (maybe no. of staff?)
### (var 11) (capital)
# 0% 25% 50% 75% 100%
# 0.00e+00 1.00e+06 1.00e+07 1.50e+07 1.75e+11
###(var 12) (credit proposal)
#0% 25% 50% 75% 100%
#0.0e+00 0.0e+00 1.0e+07 2.5e+07 5.0e+09
###(var 13) STATUS
#"Belum" "Sudah"
#"Not Yet" "Already"
###(var 14) randomid
foo_badan <- foo[which(foo$gender == "BADAN USAHA"), ]
foo_people <- foo[-which(foo$gender == "BADAN USAHA"), ]######## CODE MODIFICATION ########
X = data.frame(foo$R_date_of_birth, foo$district_code1,
foo$worker, foo$capital, foo$credit_proposal,
foo$worker_transformed, foo$capital_transformed, foo$credit_proposal_transformed,
foo$missing_date_of_birth,
foo$NA_district_code,
foo$NA_capital,
foo$NA_credit_proposal)
Tr <- foo$treat
BalanceMat <- X
genout <- GenMatch(Tr=Tr, X=X, BalanceMatrix=BalanceMat, estimand="ATT", M=1,
pop.size=5, max.generations=2, wait.generations=1,
caliper = c(0.000305876,1e+16,
1e+16,1e+16,1e+16,
1e+16,1e+16,1e+16,
1e+16,
1e+16,
1e+16,
1e+16),
exact = c(FALSE, TRUE,
FALSE, FALSE, FALSE,
TRUE, TRUE, TRUE,
TRUE,
TRUE,
TRUE,
TRUE))##
##
## Sun Apr 10 22:43:21 2022
## Domains:
## 0.000000e+00 <= X1 <= 1.000000e+03
## 0.000000e+00 <= X2 <= 1.000000e+03
## 0.000000e+00 <= X3 <= 1.000000e+03
## 0.000000e+00 <= X4 <= 1.000000e+03
## 0.000000e+00 <= X5 <= 1.000000e+03
## 0.000000e+00 <= X6 <= 1.000000e+03
## 0.000000e+00 <= X7 <= 1.000000e+03
## 0.000000e+00 <= X8 <= 1.000000e+03
## 0.000000e+00 <= X9 <= 1.000000e+03
## 0.000000e+00 <= X10 <= 1.000000e+03
## 0.000000e+00 <= X11 <= 1.000000e+03
## 0.000000e+00 <= X12 <= 1.000000e+03
## NOTE: population size is not an even number
## increasing population size by 1
##
## Data Type: Floating Point
## Operators (code number, name, population)
## (1) Cloning........................... 5
## (2) Uniform Mutation.................. 0
## (3) Boundary Mutation................. 0
## (4) Non-Uniform Mutation.............. 0
## (5) Polytope Crossover................ 0
## (6) Simple Crossover.................. 0
## (7) Whole Non-Uniform Mutation........ 0
## (8) Heuristic Crossover............... 0
## (9) Local-Minimum Crossover........... 0
##
## SOFT Maximum Number of Generations: 2
## Maximum Nonchanging Generations: 1
## Population size : 6
## Convergence Tolerance: 1.000000e-03
##
## Not Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.
## Not Checking Gradients before Stopping.
## Using Out of Bounds Individuals.
##
## Maximization Problem.
## GENERATION: 0 (initializing the population)
## Lexical Fit..... 3.424245e-09 2.141311e-07 1.492337e-03 3.368405e-01 5.872186e-01 7.548118e-01 9.997938e-01 9.999997e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 6, #Total UniqueCount: 6
## var 1:
## best............ 1.426833e+02
## mean............ 3.680701e+02
## variance........ 7.044706e+04
## var 2:
## best............ 7.667203e+02
## mean............ 4.788798e+02
## variance........ 6.084645e+04
## var 3:
## best............ 6.734909e+02
## mean............ 4.325326e+02
## variance........ 9.415470e+04
## var 4:
## best............ 7.125935e+02
## mean............ 3.654580e+02
## variance........ 6.346254e+04
## var 5:
## best............ 5.628359e+02
## mean............ 5.412994e+02
## variance........ 9.172148e+04
## var 6:
## best............ 4.500383e+02
## mean............ 3.280085e+02
## variance........ 3.788392e+04
## var 7:
## best............ 7.559536e+02
## mean............ 5.545150e+02
## variance........ 1.266630e+05
## var 8:
## best............ 2.595235e+02
## mean............ 4.283052e+02
## variance........ 8.637137e+04
## var 9:
## best............ 5.564301e+02
## mean............ 4.307669e+02
## variance........ 1.023857e+05
## var 10:
## best............ 4.522439e+02
## mean............ 4.628556e+02
## variance........ 9.587470e+04
## var 11:
## best............ 9.667644e+02
## mean............ 4.773605e+02
## variance........ 1.270421e+05
## var 12:
## best............ 6.112426e+02
## mean............ 4.306960e+02
## variance........ 1.034021e+05
##
## GENERATION: 1
## Lexical Fit..... 3.424245e-09 2.141311e-07 1.492337e-03 3.368405e-01 5.872186e-01 7.548118e-01 9.997938e-01 9.999997e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 0, #Total UniqueCount: 6
## var 1:
## best............ 1.426833e+02
## mean............ 3.680701e+02
## variance........ 7.044706e+04
## var 2:
## best............ 7.667203e+02
## mean............ 4.788798e+02
## variance........ 6.084645e+04
## var 3:
## best............ 6.734909e+02
## mean............ 4.325326e+02
## variance........ 9.415470e+04
## var 4:
## best............ 7.125935e+02
## mean............ 3.654580e+02
## variance........ 6.346254e+04
## var 5:
## best............ 5.628359e+02
## mean............ 5.412994e+02
## variance........ 9.172148e+04
## var 6:
## best............ 4.500383e+02
## mean............ 3.280085e+02
## variance........ 3.788392e+04
## var 7:
## best............ 7.559536e+02
## mean............ 5.545150e+02
## variance........ 1.266630e+05
## var 8:
## best............ 2.595235e+02
## mean............ 4.283052e+02
## variance........ 8.637137e+04
## var 9:
## best............ 5.564301e+02
## mean............ 4.307669e+02
## variance........ 1.023857e+05
## var 10:
## best............ 4.522439e+02
## mean............ 4.628556e+02
## variance........ 9.587470e+04
## var 11:
## best............ 9.667644e+02
## mean............ 4.773605e+02
## variance........ 1.270421e+05
## var 12:
## best............ 6.112426e+02
## mean............ 4.306960e+02
## variance........ 1.034021e+05
##
## GENERATION: 2
## Lexical Fit..... 3.424245e-09 2.141311e-07 1.492337e-03 3.368405e-01 5.872186e-01 7.548118e-01 9.997938e-01 9.999997e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## #unique......... 0, #Total UniqueCount: 6
## var 1:
## best............ 1.426833e+02
## mean............ 3.680701e+02
## variance........ 7.044706e+04
## var 2:
## best............ 7.667203e+02
## mean............ 4.788798e+02
## variance........ 6.084645e+04
## var 3:
## best............ 6.734909e+02
## mean............ 4.325326e+02
## variance........ 9.415470e+04
## var 4:
## best............ 7.125935e+02
## mean............ 3.654580e+02
## variance........ 6.346254e+04
## var 5:
## best............ 5.628359e+02
## mean............ 5.412994e+02
## variance........ 9.172148e+04
## var 6:
## best............ 4.500383e+02
## mean............ 3.280085e+02
## variance........ 3.788392e+04
## var 7:
## best............ 7.559536e+02
## mean............ 5.545150e+02
## variance........ 1.266630e+05
## var 8:
## best............ 2.595235e+02
## mean............ 4.283052e+02
## variance........ 8.637137e+04
## var 9:
## best............ 5.564301e+02
## mean............ 4.307669e+02
## variance........ 1.023857e+05
## var 10:
## best............ 4.522439e+02
## mean............ 4.628556e+02
## variance........ 9.587470e+04
## var 11:
## best............ 9.667644e+02
## mean............ 4.773605e+02
## variance........ 1.270421e+05
## var 12:
## best............ 6.112426e+02
## mean............ 4.306960e+02
## variance........ 1.034021e+05
##
## 'wait.generations' limit reached.
## No significant improvement in 1 generations.
##
## Solution Lexical Fitness Value:
## 3.424245e-09 2.141311e-07 1.492337e-03 3.368405e-01 5.872186e-01 7.548118e-01 9.997938e-01 9.999997e-01 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
##
## Parameters at the Solution:
##
## X[ 1] : 1.426833e+02
## X[ 2] : 7.667203e+02
## X[ 3] : 6.734909e+02
## X[ 4] : 7.125935e+02
## X[ 5] : 5.628359e+02
## X[ 6] : 4.500383e+02
## X[ 7] : 7.559536e+02
## X[ 8] : 2.595235e+02
## X[ 9] : 5.564301e+02
## X[10] : 4.522439e+02
## X[11] : 9.667644e+02
## X[12] : 6.112426e+02
##
## Solution Found Generation 1
## Number of Generations Run 2
##
## Sun Apr 10 22:49:12 2022
## Total run time : 0 hours 5 minutes and 51 secondsmout <- Match(Tr=Tr, X=X, estimand="ATT", M=1,
exact = c(FALSE, TRUE,
FALSE, FALSE, FALSE,
TRUE, TRUE, TRUE,
TRUE,
TRUE,
TRUE,
TRUE),
caliper = c(0.000305876, 1e+16,
1e+16,1e+16,1e+16,
1e+16,1e+16,1e+16,
1e+16,
1e+16,
1e+16,
1e+16),
Weight.matrix = genout)
summary(mout)##
## Estimate... 0
## SE......... 0
## T-stat..... NaN
## p.val...... NA
##
## Original number of observations.............. 76771
## Original number of treated obs............... 15873
## Matched number of observations............... 15004
## Matched number of observations (unweighted). 508234
##
## Number of obs dropped by 'exact' or 'caliper' 869mb <- MatchBalance(foo$treat~
foo$R_date_of_birth + foo$district_code1 +
foo$worker + foo$capital + foo$credit_proposal +
foo$worker_transformed + foo$capital_transformed +
foo$credit_proposal_transformed +
foo$missing_date_of_birth +
foo$NA_district_code +
foo$NA_capital +
foo$NA_credit_proposal,
match.out=mout, nboots=500)##
## ***** (V1) foo$R_date_of_birth *****
## Before Matching After Matching
## mean treatment........ -139141 -143389
## mean control.......... -139530 -143388
## std mean diff......... 0.032586 -6.9709e-05
##
## mean raw eQQ diff..... 1726.9 12.814
## med raw eQQ diff..... 968 9
## max raw eQQ diff..... 9346761 298
##
## mean eCDF diff........ 0.054932 0.00072914
## med eCDF diff........ 0.053894 0.0005647
## max eCDF diff........ 0.11396 0.003764
##
## var ratio (Tr/Co)..... 1.0052 1
## T-test p-value........ 0.97082 0.58722
## KS Bootstrap p-value.. < 2.22e-16 0.002
## KS Naive p-value...... < 2.22e-16 0.0014923
## KS Statistic.......... 0.11396 0.003764
##
##
## ***** (V2) foo$district_code1 *****
## Before Matching After Matching
## mean treatment........ 46.161 46.385
## mean control.......... 47.654 46.385
## std mean diff......... -8.5508 0
##
## mean raw eQQ diff..... 1.635 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 17 0
##
## mean eCDF diff........ 0.027068 0
## med eCDF diff........ 0.01123 0
## max eCDF diff........ 0.092468 0
##
## var ratio (Tr/Co)..... 0.97043 1
## T-test p-value........ < 2.22e-16 1
## KS Bootstrap p-value.. < 2.22e-16 1
## KS Naive p-value...... < 2.22e-16 1
## KS Statistic.......... 0.092468 1.5247e-20
##
##
## ***** (V3) foo$worker *****
## Before Matching After Matching
## mean treatment........ 2.821 2.7558
## mean control.......... 2.2934 2.7363
## std mean diff......... 2.7531 0.10936
##
## mean raw eQQ diff..... 0.52183 0.001883
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 123 20
##
## mean eCDF diff........ 0.0013569 6.7717e-06
## med eCDF diff........ 0.0008236 1.9676e-06
## max eCDF diff........ 0.067931 0.00052928
##
## var ratio (Tr/Co)..... 1.3944 1.0237
## T-test p-value........ 0.0014557 0.33684
## KS Bootstrap p-value.. < 2.22e-16 0.728
## KS Naive p-value...... < 2.22e-16 1
## KS Statistic.......... 0.067931 0.00052928
##
##
## ***** (V4) foo$capital *****
## Before Matching After Matching
## mean treatment........ 24968674 24831197
## mean control.......... 21207819 21481127
## std mean diff......... 4.0379 3.9428
##
## mean raw eQQ diff..... 17180298 162416
## med raw eQQ diff..... 2e+06 0
## max raw eQQ diff..... 1.69e+11 3.5e+09
##
## mean eCDF diff........ 0.042925 0.00033311
## med eCDF diff........ 0.046607 0.00024988
## max eCDF diff........ 0.085326 0.001336
##
## var ratio (Tr/Co)..... 0.01703 2.4914
## T-test p-value........ 0.20773 3.4242e-09
## KS Bootstrap p-value.. < 2.22e-16 0.354
## KS Naive p-value...... < 2.22e-16 0.75481
## KS Statistic.......... 0.085326 0.001336
##
##
## ***** (V5) foo$credit_proposal *****
## Before Matching After Matching
## mean treatment........ 15613005 15543926
## mean control.......... 12550889 13615535
## std mean diff......... 5.8477 3.6451
##
## mean raw eQQ diff..... 2987047 91449
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 2.5e+09 4.5e+09
##
## mean eCDF diff........ 0.029333 0.00013409
## med eCDF diff........ 0.029123 0.00010625
## max eCDF diff........ 0.065598 0.00068079
##
## var ratio (Tr/Co)..... 4.7388 6.1864
## T-test p-value........ 7.6383e-13 2.1413e-07
## KS Bootstrap p-value.. < 2.22e-16 0.846
## KS Naive p-value...... < 2.22e-16 0.99979
## KS Statistic.......... 0.065598 0.00068079
##
##
## ***** (V6) foo$worker_transformed *****
## Before Matching After Matching
## mean treatment........ 2.4636 2.4266
## mean control.......... 2.3237 2.4266
## std mean diff......... 10.162 0
##
## mean raw eQQ diff..... 0.13973 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 0
##
## mean eCDF diff........ 0.015539 0
## med eCDF diff........ 0.010607 0
## max eCDF diff........ 0.067931 0
##
## var ratio (Tr/Co)..... 1.0915 1
## T-test p-value........ < 2.22e-16 1
## KS Bootstrap p-value.. < 2.22e-16 1
## KS Naive p-value...... < 2.22e-16 1
## KS Statistic.......... 0.067931 1.5247e-20
##
##
## ***** (V7) foo$capital_transformed *****
## Before Matching After Matching
## mean treatment........ 5.9757 5.9577
## mean control.......... 5.7172 5.9577
## std mean diff......... 9.9841 0
##
## mean raw eQQ diff..... 0.29805 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 8 0
##
## mean eCDF diff........ 0.035096 0
## med eCDF diff........ 0.044062 0
## max eCDF diff........ 0.050337 0
##
## var ratio (Tr/Co)..... 0.85039 1
## T-test p-value........ < 2.22e-16 1
## KS Bootstrap p-value.. < 2.22e-16 1
## KS Naive p-value...... < 2.22e-16 1
## KS Statistic.......... 0.050337 1.5247e-20
##
##
## ***** (V8) foo$credit_proposal_transformed *****
## Before Matching After Matching
## mean treatment........ 4.679 4.6738
## mean control.......... 4.1435 4.6738
## std mean diff......... 18.605 0
##
## mean raw eQQ diff..... 0.53544 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 4 0
##
## mean eCDF diff........ 0.055941 0
## med eCDF diff........ 0.060253 0
## max eCDF diff........ 0.099595 0
##
## var ratio (Tr/Co)..... 1.1027 1
## T-test p-value........ < 2.22e-16 1
## KS Bootstrap p-value.. < 2.22e-16 1
## KS Naive p-value...... < 2.22e-16 1
## KS Statistic.......... 0.099595 1.5247e-20
##
##
## ***** (V9) foo$missing_date_of_birth *****
## Before Matching After Matching
## mean treatment........ 0.01449 0.014929
## mean control.......... 0.014418 0.014929
## std mean diff......... 0.060638 0
##
## mean raw eQQ diff..... 6.3e-05 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 0
##
## mean eCDF diff........ 3.6232e-05 0
## med eCDF diff........ 3.6232e-05 0
## max eCDF diff........ 7.2464e-05 0
##
## var ratio (Tr/Co)..... 1.005 1
## T-test p-value........ 0.94573 1
##
##
## ***** (V10) foo$NA_district_code *****
## Before Matching After Matching
## mean treatment........ 0 0
## mean control.......... 0 0
## std mean diff......... 0 0
##
## mean raw eQQ diff..... 0 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 0 0
##
## mean eCDF diff........ 0 0
## med eCDF diff........ 0 0
## max eCDF diff........ 0 0
##
## var ratio (Tr/Co)..... NaN NaN
## T-test p-value........ 1 1
##
##
## ***** (V11) foo$NA_capital *****
## Before Matching After Matching
## mean treatment........ 0.022239 0.018862
## mean control.......... 0.024713 0.018862
## std mean diff......... -1.678 0
##
## mean raw eQQ diff..... 0.002457 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 0
##
## mean eCDF diff........ 0.0012372 0
## med eCDF diff........ 0.0012372 0
## max eCDF diff........ 0.0024744 0
##
## var ratio (Tr/Co)..... 0.9022 1
## T-test p-value........ 0.062598 1
##
##
## ***** (V12) foo$NA_credit_proposal *****
## Before Matching After Matching
## mean treatment........ 0.24356 0.24307
## mean control.......... 0.17695 0.24307
## std mean diff......... 15.517 0
##
## mean raw eQQ diff..... 0.066591 0
## med raw eQQ diff..... 0 0
## max raw eQQ diff..... 1 0
##
## mean eCDF diff........ 0.033303 0
## med eCDF diff........ 0.033303 0
## max eCDF diff........ 0.066607 0
##
## var ratio (Tr/Co)..... 1.2651 1
## T-test p-value........ < 2.22e-16 1
##
##
## Before Matching Minimum p.value: < 2.22e-16
## Variable Name(s): foo$R_date_of_birth foo$district_code1 foo$worker foo$capital foo$credit_proposal foo$worker_transformed foo$capital_transformed foo$credit_proposal_transformed foo$NA_credit_proposal Number(s): 1 2 3 4 5 6 7 8 12
##
## After Matching Minimum p.value: 3.4242e-09
## Variable Name(s): foo$capital Number(s): 4