For the Safer Transit Options for Passengers field experiment, CRRC-Georgia interviewers observed about 360 minibus trips. However, some routes observed in the treatment and control groups were found in both groups or observed multiple times within the same wave. Given this, a number of observations were excluded when performing inferential statistics.

The logic of observation exclusion is as follows.Only one observation was kept per wave of observation. Hence, if a minibus was observed twice in the second round of observations, only one observation was kept. Only the first observation was kept, given that a person riding on a minibus and immediately returning on the same bus would likely arouse driver suspicion.In one case, a minibus was observed five times in total. The same logic was applied in this case, with only the first observation kept per wave.

Besides this issue, a number of minibuses were not found at the second measureement phase.Given this issue, multivariate matching with genetic weights was used in the analysis.

If you are interested in conducting a similar experiment and want to hear about some of our lessons learnt from conducting the trial, get in touch and we are happy to have a conversation.

After excluding problematic observations, there were 68 in the first wave control group, 103 in the treatment group second wave of observation, 60 in the third wave of observation treatment group, and 107 in the new or former control group.

stopsub<-subset(stop, keep==1)
table(stopsub$group)

              1st wave - Control             2nd wave - Treatment 
                              68                              103 
     3rd wave - Former treatment 3rd wave - New or former control 
                              60                              107 

The code below was used for subsetting and matching.

genoutTC <- GenMatch(Tr=stopsubt1c1$treat, X=XTC, int.seed = 42, unif.seed = 43,
                     BalanceMatrix=BalmatTC, estimand="ATT",
                     pop.size=500)


Mon Jun 19 13:44:02 2017
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 

Data Type: Floating Point
Operators (code number, name, population) 
    (1) Cloning...........................  65
    (2) Uniform Mutation..................  62
    (3) Boundary Mutation.................  62
    (4) Non-Uniform Mutation..............  62
    (5) Polytope Crossover................  62
    (6) Simple Crossover..................  62
    (7) Whole Non-Uniform Mutation........  62
    (8) Heuristic Crossover...............  62
    (9) Local-Minimum Crossover...........  0

SOFT Maximum Number of Generations: 100
Maximum Nonchanging Generations: 4
Population size       : 500
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..... 3.173278e-01  3.173278e-01  3.459257e-01  5.647782e-01  6.553562e-01  7.486393e-01  7.635372e-01  8.114438e-01  9.307857e-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  
#unique......... 500, #Total UniqueCount: 500
var 1:
best............ 2.606886e+02
mean............ 5.171244e+02
variance........ 7.903943e+04
var 2:
best............ 4.235670e+02
mean............ 5.081839e+02
variance........ 8.396387e+04
var 3:
best............ 1.024939e+02
mean............ 4.937976e+02
variance........ 8.388562e+04
var 4:
best............ 9.944659e+02
mean............ 5.144284e+02
variance........ 9.014212e+04
var 5:
best............ 4.152015e+02
mean............ 4.980008e+02
variance........ 8.145723e+04
var 6:
best............ 2.822566e+02
mean............ 5.023210e+02
variance........ 8.209425e+04
var 7:
best............ 6.150468e+02
mean............ 5.068778e+02
variance........ 8.157006e+04
var 8:
best............ 1.004543e+02
mean............ 5.026291e+02
variance........ 7.858707e+04
var 9:
best............ 3.320248e+02
mean............ 4.872606e+02
variance........ 8.669141e+04

GENERATION: 1
Lexical Fit..... 3.173278e-01  3.173278e-01  4.673775e-01  5.239314e-01  5.277034e-01  6.960422e-01  7.819933e-01  8.513809e-01  9.307857e-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  
#unique......... 374, #Total UniqueCount: 874
var 1:
best............ 8.757689e+01
mean............ 4.810613e+02
variance........ 7.710139e+04
var 2:
best............ 4.770403e+02
mean............ 4.287192e+02
variance........ 3.363917e+04
var 3:
best............ 1.024939e+02
mean............ 4.115662e+02
variance........ 1.231191e+05
var 4:
best............ 9.944659e+02
mean............ 6.987016e+02
variance........ 9.636325e+04
var 5:
best............ 4.152015e+02
mean............ 4.413568e+02
variance........ 6.643159e+04
var 6:
best............ 2.822566e+02
mean............ 2.940022e+02
variance........ 4.591817e+04
var 7:
best............ 3.926427e+02
mean............ 5.908575e+02
variance........ 3.730215e+04
var 8:
best............ 1.004543e+02
mean............ 3.200436e+02
variance........ 6.906415e+04
var 9:
best............ 3.949055e+02
mean............ 4.512266e+02
variance........ 5.455725e+04

GENERATION: 2
Lexical Fit..... 3.173278e-01  3.173278e-01  4.673775e-01  5.277034e-01  5.372285e-01  7.536431e-01  7.561366e-01  7.819933e-01  8.551800e-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  
#unique......... 364, #Total UniqueCount: 1238
var 1:
best............ 6.289898e+01
mean............ 2.473429e+02
variance........ 3.236648e+04
var 2:
best............ 4.648237e+02
mean............ 4.595762e+02
variance........ 1.193002e+04
var 3:
best............ 9.243663e+01
mean............ 2.241728e+02
variance........ 5.252614e+04
var 4:
best............ 9.989501e+02
mean............ 8.735020e+02
variance........ 5.544052e+04
var 5:
best............ 3.939693e+02
mean............ 5.004610e+02
variance........ 3.219015e+04
var 6:
best............ 2.822566e+02
mean............ 2.643432e+02
variance........ 7.680905e+03
var 7:
best............ 3.581408e+02
mean............ 5.023881e+02
variance........ 2.678792e+04
var 8:
best............ 9.132340e+01
mean............ 1.679660e+02
variance........ 2.280924e+04
var 9:
best............ 3.898034e+02
mean............ 3.806258e+02
variance........ 1.481686e+04

GENERATION: 3
Lexical Fit..... 3.173278e-01  3.173278e-01  4.714626e-01  5.277034e-01  5.643451e-01  5.643451e-01  5.790506e-01  6.177256e-01  7.486393e-01  9.307857e-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  
#unique......... 380, #Total UniqueCount: 1618
var 1:
best............ 4.790830e+01
mean............ 1.150631e+02
variance........ 1.720634e+04
var 2:
best............ 2.247880e+02
mean............ 4.685826e+02
variance........ 6.065031e+03
var 3:
best............ 9.167288e+01
mean............ 1.365159e+02
variance........ 1.453680e+04
var 4:
best............ 9.993125e+02
mean............ 9.649084e+02
variance........ 1.173569e+04
var 5:
best............ 2.971284e+02
mean............ 4.113419e+02
variance........ 8.276055e+03
var 6:
best............ 2.616409e+02
mean............ 2.957254e+02
variance........ 9.087811e+03
var 7:
best............ 5.073620e+02
mean............ 3.897420e+02
variance........ 9.570855e+03
var 8:
best............ 9.063001e+01
mean............ 1.227077e+02
variance........ 1.006116e+04
var 9:
best............ 3.816470e+02
mean............ 3.921649e+02
variance........ 4.944157e+03

GENERATION: 4
Lexical Fit..... 3.173278e-01  3.173278e-01  4.714626e-01  5.277034e-01  5.643451e-01  5.643451e-01  5.790506e-01  6.177256e-01  7.486393e-01  9.307857e-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  
#unique......... 373, #Total UniqueCount: 1991
var 1:
best............ 4.790830e+01
mean............ 9.927800e+01
variance........ 1.065320e+04
var 2:
best............ 2.247880e+02
mean............ 3.853644e+02
variance........ 1.955359e+04
var 3:
best............ 9.167288e+01
mean............ 1.293441e+02
variance........ 1.351842e+04
var 4:
best............ 9.993125e+02
mean............ 9.625446e+02
variance........ 1.346024e+04
var 5:
best............ 2.971284e+02
mean............ 3.744995e+02
variance........ 8.569348e+03
var 6:
best............ 2.616409e+02
mean............ 2.920933e+02
variance........ 6.950492e+03
var 7:
best............ 5.073620e+02
mean............ 4.487852e+02
variance........ 9.838315e+03
var 8:
best............ 9.063001e+01
mean............ 1.217875e+02
variance........ 1.154255e+04
var 9:
best............ 3.816470e+02
mean............ 4.004230e+02
variance........ 6.084087e+03

GENERATION: 5
Lexical Fit..... 3.173278e-01  3.173278e-01  4.861646e-01  5.277034e-01  5.643451e-01  5.643451e-01  6.177256e-01  6.429956e-01  7.435446e-01  9.283959e-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  
#unique......... 328, #Total UniqueCount: 2319
var 1:
best............ 4.790830e+01
mean............ 8.089396e+01
variance........ 1.114031e+04
var 2:
best............ 2.247880e+02
mean............ 2.714366e+02
variance........ 1.323016e+04
var 3:
best............ 9.167288e+01
mean............ 1.125017e+02
variance........ 7.831401e+03
var 4:
best............ 6.616347e+02
mean............ 9.493697e+02
variance........ 1.679304e+04
var 5:
best............ 4.488897e+02
mean............ 3.249642e+02
variance........ 7.158492e+03
var 6:
best............ 2.581231e+02
mean............ 2.737767e+02
variance........ 6.027826e+03
var 7:
best............ 5.073620e+02
mean............ 5.073977e+02
variance........ 7.661149e+03
var 8:
best............ 6.506063e+01
mean............ 1.125423e+02
variance........ 7.055072e+03
var 9:
best............ 3.816470e+02
mean............ 3.913250e+02
variance........ 5.390984e+03

GENERATION: 6
Lexical Fit..... 3.173278e-01  3.173278e-01  4.935820e-01  5.277034e-01  5.643451e-01  5.643451e-01  6.177256e-01  6.794550e-01  8.474885e-01  9.283959e-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  
#unique......... 351, #Total UniqueCount: 2670
var 1:
best............ 4.790830e+01
mean............ 7.853468e+01
variance........ 1.128762e+04
var 2:
best............ 2.247880e+02
mean............ 2.387745e+02
variance........ 5.989338e+03
var 3:
best............ 9.167288e+01
mean............ 1.152101e+02
variance........ 8.099268e+03
var 4:
best............ 6.616347e+02
mean............ 8.182940e+02
variance........ 3.838792e+04
var 5:
best............ 4.488897e+02
mean............ 3.813352e+02
variance........ 1.453897e+04
var 6:
best............ 2.581231e+02
mean............ 2.741261e+02
variance........ 6.854631e+03
var 7:
best............ 5.073620e+02
mean............ 5.122161e+02
variance........ 6.114874e+03
var 8:
best............ 4.655527e+01
mean............ 1.073264e+02
variance........ 1.065741e+04
var 9:
best............ 3.816470e+02
mean............ 3.857435e+02
variance........ 3.593922e+03

GENERATION: 7
Lexical Fit..... 3.173278e-01  3.173278e-01  4.935820e-01  5.277034e-01  5.643451e-01  5.643451e-01  6.177256e-01  6.794550e-01  8.474885e-01  9.283959e-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  
#unique......... 343, #Total UniqueCount: 3013
var 1:
best............ 4.790830e+01
mean............ 7.445427e+01
variance........ 9.383002e+03
var 2:
best............ 2.247880e+02
mean............ 2.395051e+02
variance........ 7.166724e+03
var 3:
best............ 9.167288e+01
mean............ 1.178155e+02
variance........ 9.372860e+03
var 4:
best............ 6.616347e+02
mean............ 6.965796e+02
variance........ 1.673061e+04
var 5:
best............ 4.488897e+02
mean............ 4.462433e+02
variance........ 1.016616e+04
var 6:
best............ 2.581231e+02
mean............ 2.683946e+02
variance........ 4.149908e+03
var 7:
best............ 5.073620e+02
mean............ 5.307372e+02
variance........ 9.110194e+03
var 8:
best............ 4.655527e+01
mean............ 7.477347e+01
variance........ 6.898043e+03
var 9:
best............ 3.816470e+02
mean............ 3.877396e+02
variance........ 5.074900e+03

GENERATION: 8
Lexical Fit..... 3.173278e-01  3.173278e-01  4.935820e-01  5.277034e-01  5.643451e-01  5.643451e-01  6.177256e-01  6.794550e-01  8.474885e-01  9.283959e-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  
#unique......... 323, #Total UniqueCount: 3336
var 1:
best............ 4.790830e+01
mean............ 7.471715e+01
variance........ 9.682644e+03
var 2:
best............ 2.247880e+02
mean............ 2.436225e+02
variance........ 7.670771e+03
var 3:
best............ 9.167288e+01
mean............ 1.168467e+02
variance........ 1.030398e+04
var 4:
best............ 6.616347e+02
mean............ 6.414147e+02
variance........ 8.087683e+03
var 5:
best............ 4.488897e+02
mean............ 4.623834e+02
variance........ 5.866467e+03
var 6:
best............ 2.581231e+02
mean............ 2.729294e+02
variance........ 6.709334e+03
var 7:
best............ 5.073620e+02
mean............ 5.474364e+02
variance........ 1.364905e+04
var 8:
best............ 4.655527e+01
mean............ 7.208850e+01
variance........ 8.661516e+03
var 9:
best............ 3.816470e+02
mean............ 3.885857e+02
variance........ 3.835789e+03

GENERATION: 9
Lexical Fit..... 3.173278e-01  3.173278e-01  4.935820e-01  5.277034e-01  5.643451e-01  5.643451e-01  6.177256e-01  6.794550e-01  8.474885e-01  9.283959e-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  
#unique......... 315, #Total UniqueCount: 3651
var 1:
best............ 4.790830e+01
mean............ 6.557785e+01
variance........ 5.997188e+03
var 2:
best............ 2.247880e+02
mean............ 2.421201e+02
variance........ 6.105496e+03
var 3:
best............ 9.167288e+01
mean............ 1.127661e+02
variance........ 5.853147e+03
var 4:
best............ 6.616347e+02
mean............ 6.430378e+02
variance........ 6.643790e+03
var 5:
best............ 4.488897e+02
mean............ 4.567131e+02
variance........ 6.877043e+03
var 6:
best............ 2.581231e+02
mean............ 2.650235e+02
variance........ 3.120156e+03
var 7:
best............ 5.073620e+02
mean............ 5.331444e+02
variance........ 1.543139e+04
var 8:
best............ 4.655527e+01
mean............ 6.711991e+01
variance........ 6.910479e+03
var 9:
best............ 3.816470e+02
mean............ 3.905021e+02
variance........ 3.565642e+03

GENERATION: 10
Lexical Fit..... 3.173278e-01  3.173278e-01  4.935820e-01  5.277034e-01  5.643451e-01  5.643451e-01  6.177256e-01  6.794550e-01  8.474885e-01  9.283959e-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  
#unique......... 327, #Total UniqueCount: 3978
var 1:
best............ 4.790830e+01
mean............ 6.886224e+01
variance........ 7.095058e+03
var 2:
best............ 2.247880e+02
mean............ 2.410603e+02
variance........ 5.358780e+03
var 3:
best............ 9.167288e+01
mean............ 1.049175e+02
variance........ 4.629343e+03
var 4:
best............ 6.616347e+02
mean............ 6.449597e+02
variance........ 5.743597e+03
var 5:
best............ 4.488897e+02
mean............ 4.585369e+02
variance........ 6.572273e+03
var 6:
best............ 2.581231e+02
mean............ 2.701275e+02
variance........ 6.231017e+03
var 7:
best............ 5.073620e+02
mean............ 5.319544e+02
variance........ 1.342320e+04
var 8:
best............ 4.655527e+01
mean............ 6.476981e+01
variance........ 5.212030e+03
var 9:
best............ 3.816470e+02
mean............ 3.880054e+02
variance........ 4.702423e+03

GENERATION: 11
Lexical Fit..... 3.173278e-01  3.173278e-01  4.935820e-01  5.277034e-01  5.643451e-01  5.643451e-01  6.177256e-01  6.794550e-01  8.474885e-01  9.283959e-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  
#unique......... 322, #Total UniqueCount: 4300
var 1:
best............ 4.790830e+01
mean............ 6.278663e+01
variance........ 4.329163e+03
var 2:
best............ 2.247880e+02
mean............ 2.344916e+02
variance........ 4.784053e+03
var 3:
best............ 9.167288e+01
mean............ 1.183906e+02
variance........ 9.616384e+03
var 4:
best............ 6.616347e+02
mean............ 6.508692e+02
variance........ 4.609120e+03
var 5:
best............ 4.488897e+02
mean............ 4.523580e+02
variance........ 5.010475e+03
var 6:
best............ 2.581231e+02
mean............ 2.669598e+02
variance........ 4.456690e+03
var 7:
best............ 5.073620e+02
mean............ 5.410125e+02
variance........ 1.679299e+04
var 8:
best............ 4.655527e+01
mean............ 6.419624e+01
variance........ 6.570300e+03
var 9:
best............ 3.816470e+02
mean............ 3.914997e+02
variance........ 4.818498e+03

'wait.generations' limit reached.
No significant improvement in 4 generations.

Solution Lexical Fitness Value:
3.173278e-01  3.173278e-01  4.935820e-01  5.277034e-01  5.643451e-01  5.643451e-01  6.177256e-01  6.794550e-01  8.474885e-01  9.283959e-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  

Parameters at the Solution:

 X[ 1] :    4.790830e+01
 X[ 2] :    2.247880e+02
 X[ 3] :    9.167288e+01
 X[ 4] :    6.616347e+02
 X[ 5] :    4.488897e+02
 X[ 6] :    2.581231e+02
 X[ 7] :    5.073620e+02
 X[ 8] :    4.655527e+01
 X[ 9] :    3.816470e+02

Solution Found Generation 6
Number of Generations Run 11

Mon Jun 19 13:44:40 2017
Total run time : 0 hours 0 minutes and 38 seconds
genoutCC <- GenMatch(Tr=stopsubc1c2$treat, X=XCC, int.seed = 42, unif.seed = 43,
                     BalanceMatrix=BalmatCC, estimand="ATT",
                     pop.size=500)


Mon Jun 19 13:44:40 2017
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 

Data Type: Floating Point
Operators (code number, name, population) 
    (1) Cloning...........................  65
    (2) Uniform Mutation..................  62
    (3) Boundary Mutation.................  62
    (4) Non-Uniform Mutation..............  62
    (5) Polytope Crossover................  62
    (6) Simple Crossover..................  62
    (7) Whole Non-Uniform Mutation........  62
    (8) Heuristic Crossover...............  62
    (9) Local-Minimum Crossover...........  0

SOFT Maximum Number of Generations: 100
Maximum Nonchanging Generations: 4
Population size       : 500
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..... 1.563411e-01  2.389096e-01  3.173265e-01  3.173265e-01  3.664400e-01  4.032473e-01  5.936067e-01  5.936067e-01  6.944515e-01  9.868228e-01  9.999442e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 500, #Total UniqueCount: 500
var 1:
best............ 3.101340e+02
mean............ 5.171244e+02
variance........ 7.903943e+04
var 2:
best............ 3.855979e+02
mean............ 5.081839e+02
variance........ 8.396387e+04
var 3:
best............ 9.871858e+01
mean............ 4.937976e+02
variance........ 8.388562e+04
var 4:
best............ 7.102864e+02
mean............ 5.144284e+02
variance........ 9.014212e+04
var 5:
best............ 6.405179e+02
mean............ 4.980008e+02
variance........ 8.145723e+04
var 6:
best............ 7.926925e+02
mean............ 5.023210e+02
variance........ 8.209425e+04
var 7:
best............ 6.892373e+00
mean............ 5.068778e+02
variance........ 8.157006e+04
var 8:
best............ 5.206789e+02
mean............ 5.026291e+02
variance........ 7.858707e+04
var 9:
best............ 5.092711e+02
mean............ 4.872606e+02
variance........ 8.669141e+04

GENERATION: 1
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  3.826742e-01  5.643207e-01  6.127165e-01  7.060257e-01  7.060257e-01  7.905487e-01  8.824458e-01  9.728057e-01  9.999326e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 374, #Total UniqueCount: 874
var 1:
best............ 9.018071e+02
mean............ 4.601653e+02
variance........ 4.311894e+04
var 2:
best............ 3.025776e+02
mean............ 3.702474e+02
variance........ 3.495995e+04
var 3:
best............ 3.218131e+02
mean............ 2.340328e+02
variance........ 2.945168e+04
var 4:
best............ 8.398381e+02
mean............ 6.586226e+02
variance........ 5.537323e+04
var 5:
best............ 9.502523e+02
mean............ 6.562180e+02
variance........ 5.022058e+04
var 6:
best............ 6.590114e+02
mean............ 6.928056e+02
variance........ 3.682225e+04
var 7:
best............ 7.139824e+01
mean............ 1.940847e+02
variance........ 6.866710e+04
var 8:
best............ 2.146901e+02
mean............ 4.337689e+02
variance........ 4.063979e+04
var 9:
best............ 2.288121e+01
mean............ 3.834709e+02
variance........ 6.384305e+04

GENERATION: 2
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.145943e-01  4.145943e-01  4.237429e-01  5.172302e-01  5.643207e-01  7.905487e-01  8.824458e-01  9.595401e-01  9.980276e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 364, #Total UniqueCount: 1238
var 1:
best............ 8.805237e+02
mean............ 6.521600e+02
variance........ 5.959554e+04
var 2:
best............ 2.851199e+02
mean............ 3.046760e+02
variance........ 1.279705e+04
var 3:
best............ 3.160483e+02
mean............ 2.619097e+02
variance........ 1.256920e+04
var 4:
best............ 8.481832e+02
mean............ 8.031822e+02
variance........ 1.201572e+04
var 5:
best............ 9.139603e+02
mean............ 8.151480e+02
variance........ 2.671979e+04
var 6:
best............ 6.134090e+02
mean............ 6.060008e+02
variance........ 2.924973e+04
var 7:
best............ 6.995136e+01
mean............ 7.842473e+01
variance........ 9.976811e+03
var 8:
best............ 1.899540e+02
mean............ 2.617053e+02
variance........ 3.339140e+04
var 9:
best............ 8.745773e+01
mean............ 2.305594e+02
variance........ 5.318169e+04

GENERATION: 3
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.145943e-01  4.145943e-01  4.237429e-01  5.516472e-01  5.643207e-01  7.989514e-01  8.884604e-01  9.254713e-01  9.982657e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 380, #Total UniqueCount: 1618
var 1:
best............ 8.805237e+02
mean............ 8.497059e+02
variance........ 1.702289e+04
var 2:
best............ 2.851199e+02
mean............ 3.009503e+02
variance........ 7.150636e+03
var 3:
best............ 3.160483e+02
mean............ 3.361820e+02
variance........ 7.590314e+03
var 4:
best............ 8.481832e+02
mean............ 8.267250e+02
variance........ 6.821887e+03
var 5:
best............ 9.139603e+02
mean............ 8.979324e+02
variance........ 1.218397e+04
var 6:
best............ 5.315865e+02
mean............ 6.167467e+02
variance........ 8.215818e+03
var 7:
best............ 6.995136e+01
mean............ 9.530290e+01
variance........ 9.954523e+03
var 8:
best............ 9.057620e+01
mean............ 2.227912e+02
variance........ 1.046447e+04
var 9:
best............ 8.745773e+01
mean............ 8.846419e+01
variance........ 1.164742e+04

GENERATION: 4
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.155595e-01  5.643207e-01  6.127165e-01  7.060257e-01  7.060257e-01  7.989514e-01  8.884604e-01  9.268988e-01  9.999442e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 355, #Total UniqueCount: 1973
var 1:
best............ 8.805237e+02
mean............ 8.391042e+02
variance........ 1.408203e+04
var 2:
best............ 2.851199e+02
mean............ 3.051026e+02
variance........ 7.987299e+03
var 3:
best............ 2.684227e+02
mean............ 3.287503e+02
variance........ 5.576276e+03
var 4:
best............ 8.481832e+02
mean............ 8.303479e+02
variance........ 5.756746e+03
var 5:
best............ 9.139603e+02
mean............ 8.875388e+02
variance........ 1.164886e+04
var 6:
best............ 5.315865e+02
mean............ 5.762327e+02
variance........ 7.467431e+03
var 7:
best............ 6.995136e+01
mean............ 1.009326e+02
variance........ 1.181037e+04
var 8:
best............ 9.057620e+01
mean............ 1.720516e+02
variance........ 1.248557e+04
var 9:
best............ 8.745773e+01
mean............ 1.019264e+02
variance........ 6.676360e+03

GENERATION: 5
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.327451e-01  5.643207e-01  6.127165e-01  7.042277e-01  7.060257e-01  7.060257e-01  8.941820e-01  9.040079e-01  9.999538e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 325, #Total UniqueCount: 2298
var 1:
best............ 8.805237e+02
mean............ 8.490469e+02
variance........ 1.263222e+04
var 2:
best............ 2.851199e+02
mean............ 3.006651e+02
variance........ 7.222560e+03
var 3:
best............ 2.684227e+02
mean............ 3.093986e+02
variance........ 7.046941e+03
var 4:
best............ 8.481832e+02
mean............ 8.201114e+02
variance........ 1.279805e+04
var 5:
best............ 9.139603e+02
mean............ 8.841062e+02
variance........ 1.230619e+04
var 6:
best............ 5.315865e+02
mean............ 5.138592e+02
variance........ 4.937936e+03
var 7:
best............ 6.995136e+01
mean............ 9.864175e+01
variance........ 1.162847e+04
var 8:
best............ 4.608001e+01
mean............ 8.893122e+01
variance........ 6.891647e+03
var 9:
best............ 8.745773e+01
mean............ 1.083197e+02
variance........ 8.005268e+03

GENERATION: 6
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.327451e-01  5.643207e-01  6.127165e-01  7.042277e-01  7.060257e-01  7.060257e-01  8.941820e-01  9.040079e-01  9.999538e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 327, #Total UniqueCount: 2625
var 1:
best............ 8.805237e+02
mean............ 8.611922e+02
variance........ 9.265100e+03
var 2:
best............ 2.851199e+02
mean............ 2.961847e+02
variance........ 5.029395e+03
var 3:
best............ 2.684227e+02
mean............ 2.861641e+02
variance........ 4.963281e+03
var 4:
best............ 8.481832e+02
mean............ 8.305515e+02
variance........ 7.987211e+03
var 5:
best............ 9.139603e+02
mean............ 8.893645e+02
variance........ 1.057940e+04
var 6:
best............ 5.315865e+02
mean............ 5.214748e+02
variance........ 5.226559e+03
var 7:
best............ 6.995136e+01
mean............ 9.404734e+01
variance........ 9.518915e+03
var 8:
best............ 4.608001e+01
mean............ 9.016496e+01
variance........ 1.087515e+04
var 9:
best............ 8.745773e+01
mean............ 1.057450e+02
variance........ 6.392798e+03

GENERATION: 7
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.330517e-01  5.643207e-01  6.127165e-01  7.060257e-01  7.060257e-01  8.070437e-01  8.941820e-01  9.039868e-01  9.999538e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 310, #Total UniqueCount: 2935
var 1:
best............ 8.805237e+02
mean............ 8.589545e+02
variance........ 6.407659e+03
var 2:
best............ 2.851199e+02
mean............ 2.956002e+02
variance........ 5.565223e+03
var 3:
best............ 2.684227e+02
mean............ 2.830978e+02
variance........ 6.604954e+03
var 4:
best............ 8.481832e+02
mean............ 8.249538e+02
variance........ 1.077977e+04
var 5:
best............ 9.139603e+02
mean............ 8.952453e+02
variance........ 5.748292e+03
var 6:
best............ 5.315865e+02
mean............ 5.279222e+02
variance........ 4.968548e+03
var 7:
best............ 6.995136e+01
mean............ 9.542723e+01
variance........ 1.089299e+04
var 8:
best............ 3.018911e+01
mean............ 7.418461e+01
variance........ 8.463445e+03
var 9:
best............ 8.745773e+01
mean............ 1.110088e+02
variance........ 9.091569e+03

GENERATION: 8
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.330517e-01  5.643207e-01  6.127165e-01  7.060257e-01  7.060257e-01  8.070437e-01  8.941820e-01  9.039868e-01  9.999538e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 364, #Total UniqueCount: 3299
var 1:
best............ 8.805237e+02
mean............ 8.580688e+02
variance........ 7.146983e+03
var 2:
best............ 2.851199e+02
mean............ 2.953421e+02
variance........ 4.500433e+03
var 3:
best............ 2.684227e+02
mean............ 2.762685e+02
variance........ 4.623679e+03
var 4:
best............ 8.481832e+02
mean............ 8.186786e+02
variance........ 1.178835e+04
var 5:
best............ 9.139603e+02
mean............ 8.871984e+02
variance........ 9.815106e+03
var 6:
best............ 5.315865e+02
mean............ 5.292173e+02
variance........ 6.322571e+03
var 7:
best............ 6.995136e+01
mean............ 9.775683e+01
variance........ 1.611520e+04
var 8:
best............ 3.018911e+01
mean............ 5.971349e+01
variance........ 8.331040e+03
var 9:
best............ 8.745773e+01
mean............ 1.151333e+02
variance........ 1.226594e+04

GENERATION: 9
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.330517e-01  5.643207e-01  6.127165e-01  7.060257e-01  7.060257e-01  8.070437e-01  8.941820e-01  9.039868e-01  9.999538e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 344, #Total UniqueCount: 3643
var 1:
best............ 8.805237e+02
mean............ 8.595441e+02
variance........ 7.851748e+03
var 2:
best............ 2.851199e+02
mean............ 2.956999e+02
variance........ 4.175412e+03
var 3:
best............ 2.684227e+02
mean............ 2.788938e+02
variance........ 4.668434e+03
var 4:
best............ 8.481832e+02
mean............ 8.243941e+02
variance........ 9.397101e+03
var 5:
best............ 9.139603e+02
mean............ 8.924557e+02
variance........ 8.325585e+03
var 6:
best............ 5.315865e+02
mean............ 5.261038e+02
variance........ 3.436030e+03
var 7:
best............ 6.995136e+01
mean............ 8.438776e+01
variance........ 9.345995e+03
var 8:
best............ 3.018911e+01
mean............ 5.585349e+01
variance........ 8.741362e+03
var 9:
best............ 8.745773e+01
mean............ 1.112010e+02
variance........ 9.756225e+03

GENERATION: 10
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.330517e-01  5.643207e-01  6.127165e-01  7.060257e-01  7.060257e-01  8.070437e-01  8.941820e-01  9.039868e-01  9.999538e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 335, #Total UniqueCount: 3978
var 1:
best............ 8.805237e+02
mean............ 8.604489e+02
variance........ 7.401594e+03
var 2:
best............ 2.851199e+02
mean............ 2.954626e+02
variance........ 4.414101e+03
var 3:
best............ 2.684227e+02
mean............ 2.810125e+02
variance........ 5.813747e+03
var 4:
best............ 8.481832e+02
mean............ 8.297928e+02
variance........ 6.668178e+03
var 5:
best............ 9.139603e+02
mean............ 8.915169e+02
variance........ 7.369234e+03
var 6:
best............ 5.315865e+02
mean............ 5.333835e+02
variance........ 2.510766e+03
var 7:
best............ 6.995136e+01
mean............ 8.242233e+01
variance........ 8.649742e+03
var 8:
best............ 3.018911e+01
mean............ 5.650590e+01
variance........ 8.848835e+03
var 9:
best............ 8.745773e+01
mean............ 1.127544e+02
variance........ 8.553667e+03

GENERATION: 11
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.330517e-01  5.643207e-01  6.127165e-01  7.060257e-01  7.060257e-01  8.070437e-01  8.941820e-01  9.039868e-01  9.999538e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 322, #Total UniqueCount: 4300
var 1:
best............ 8.805237e+02
mean............ 8.571192e+02
variance........ 8.878051e+03
var 2:
best............ 2.851199e+02
mean............ 2.922242e+02
variance........ 5.452036e+03
var 3:
best............ 2.684227e+02
mean............ 2.809552e+02
variance........ 5.509085e+03
var 4:
best............ 8.481832e+02
mean............ 8.299916e+02
variance........ 6.138235e+03
var 5:
best............ 9.139603e+02
mean............ 8.978882e+02
variance........ 6.431565e+03
var 6:
best............ 5.315865e+02
mean............ 5.278371e+02
variance........ 2.196756e+03
var 7:
best............ 6.995136e+01
mean............ 7.646112e+01
variance........ 7.993339e+03
var 8:
best............ 3.018911e+01
mean............ 4.363076e+01
variance........ 3.532826e+03
var 9:
best............ 8.745773e+01
mean............ 1.138861e+02
variance........ 9.812261e+03

GENERATION: 12
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.800264e-01  5.643207e-01  5.723046e-01  8.030357e-01  8.030357e-01  8.123071e-01  9.999492e-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  
#unique......... 329, #Total UniqueCount: 4629
var 1:
best............ 8.805237e+02
mean............ 8.644231e+02
variance........ 4.865024e+03
var 2:
best............ 2.851199e+02
mean............ 2.958996e+02
variance........ 5.108878e+03
var 3:
best............ 2.684227e+02
mean............ 2.784134e+02
variance........ 3.394515e+03
var 4:
best............ 7.377929e+02
mean............ 8.341035e+02
variance........ 4.943912e+03
var 5:
best............ 9.139603e+02
mean............ 8.923442e+02
variance........ 7.756056e+03
var 6:
best............ 5.315865e+02
mean............ 5.258597e+02
variance........ 4.054913e+03
var 7:
best............ 2.183070e+01
mean............ 7.025434e+01
variance........ 6.102788e+03
var 8:
best............ 3.101906e+01
mean............ 5.409692e+01
variance........ 8.780813e+03
var 9:
best............ 1.706481e+02
mean............ 1.086388e+02
variance........ 9.528959e+03

GENERATION: 13
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.800264e-01  5.643207e-01  5.723046e-01  8.030357e-01  8.030357e-01  8.123071e-01  9.999492e-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  
#unique......... 349, #Total UniqueCount: 4978
var 1:
best............ 8.805237e+02
mean............ 8.542871e+02
variance........ 1.090249e+04
var 2:
best............ 2.851199e+02
mean............ 3.018507e+02
variance........ 7.073573e+03
var 3:
best............ 2.684227e+02
mean............ 2.831681e+02
variance........ 6.963718e+03
var 4:
best............ 7.377929e+02
mean............ 7.779314e+02
variance........ 9.939299e+03
var 5:
best............ 9.139603e+02
mean............ 8.948724e+02
variance........ 6.596473e+03
var 6:
best............ 5.315865e+02
mean............ 5.306992e+02
variance........ 3.630381e+03
var 7:
best............ 2.183070e+01
mean............ 5.775045e+01
variance........ 8.790517e+03
var 8:
best............ 3.101906e+01
mean............ 5.215305e+01
variance........ 6.649941e+03
var 9:
best............ 1.706481e+02
mean............ 1.709542e+02
variance........ 1.278084e+04

GENERATION: 14
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.800264e-01  5.643207e-01  6.164135e-01  8.030357e-01  8.263135e-01  8.913570e-01  9.999492e-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  
#unique......... 328, #Total UniqueCount: 5306
var 1:
best............ 8.817569e+02
mean............ 8.643942e+02
variance........ 6.949476e+03
var 2:
best............ 2.851199e+02
mean............ 2.930207e+02
variance........ 3.813866e+03
var 3:
best............ 2.351721e+02
mean............ 2.821094e+02
variance........ 4.758638e+03
var 4:
best............ 7.377929e+02
mean............ 7.432600e+02
variance........ 4.821696e+03
var 5:
best............ 9.218505e+02
mean............ 9.011677e+02
variance........ 6.689659e+03
var 6:
best............ 4.874302e+02
mean............ 5.304469e+02
variance........ 4.516771e+03
var 7:
best............ 2.183070e+01
mean............ 4.185821e+01
variance........ 5.800668e+03
var 8:
best............ 1.019930e+02
mean............ 5.316364e+01
variance........ 8.062291e+03
var 9:
best............ 1.706481e+02
mean............ 1.950823e+02
variance........ 6.460012e+03

GENERATION: 15
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  4.800264e-01  5.643207e-01  6.356354e-01  8.031985e-01  8.070437e-01  8.941820e-01  9.999538e-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  
#unique......... 329, #Total UniqueCount: 5635
var 1:
best............ 8.809204e+02
mean............ 8.608098e+02
variance........ 8.065652e+03
var 2:
best............ 2.851199e+02
mean............ 2.966449e+02
variance........ 4.500072e+03
var 3:
best............ 2.577271e+02
mean............ 2.645878e+02
variance........ 5.312741e+03
var 4:
best............ 7.381369e+02
mean............ 7.327397e+02
variance........ 5.595000e+03
var 5:
best............ 9.230670e+02
mean............ 8.994451e+02
variance........ 8.087900e+03
var 6:
best............ 5.173829e+02
mean............ 5.127588e+02
variance........ 4.019518e+03
var 7:
best............ 2.183070e+01
mean............ 4.072311e+01
variance........ 7.353563e+03
var 8:
best............ 5.384895e+01
mean............ 8.667555e+01
variance........ 1.074770e+04
var 9:
best............ 1.707893e+02
mean............ 1.865426e+02
variance........ 6.196316e+03

GENERATION: 16
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  5.092679e-01  5.643207e-01  7.060257e-01  7.394108e-01  7.394108e-01  8.046187e-01  8.070437e-01  8.941820e-01  9.999999e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 374, #Total UniqueCount: 6009
var 1:
best............ 8.069966e+02
mean............ 8.609361e+02
variance........ 7.427674e+03
var 2:
best............ 2.803131e+02
mean............ 2.955828e+02
variance........ 4.072404e+03
var 3:
best............ 2.513668e+02
mean............ 2.640113e+02
variance........ 3.528967e+03
var 4:
best............ 6.805006e+02
mean............ 7.264375e+02
variance........ 5.507101e+03
var 5:
best............ 9.105738e+02
mean............ 8.918608e+02
variance........ 1.173909e+04
var 6:
best............ 5.191253e+02
mean............ 5.111923e+02
variance........ 4.191729e+03
var 7:
best............ 2.091762e+01
mean............ 4.788262e+01
variance........ 1.064558e+04
var 8:
best............ 4.863729e+01
mean............ 7.674086e+01
variance........ 6.194725e+03
var 9:
best............ 1.650151e+02
mean............ 1.811275e+02
variance........ 2.512664e+03

GENERATION: 17
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  5.092679e-01  5.643207e-01  7.060257e-01  7.394108e-01  7.394108e-01  8.046187e-01  8.070437e-01  8.941820e-01  9.999999e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 373, #Total UniqueCount: 6382
var 1:
best............ 8.069966e+02
mean............ 8.290973e+02
variance........ 7.483093e+03
var 2:
best............ 2.803131e+02
mean............ 2.915575e+02
variance........ 2.854084e+03
var 3:
best............ 2.513668e+02
mean............ 2.660586e+02
variance........ 4.088082e+03
var 4:
best............ 6.805006e+02
mean............ 7.037152e+02
variance........ 3.964185e+03
var 5:
best............ 9.105738e+02
mean............ 9.019356e+02
variance........ 5.134534e+03
var 6:
best............ 5.191253e+02
mean............ 5.179724e+02
variance........ 1.463052e+03
var 7:
best............ 2.091762e+01
mean............ 3.835037e+01
variance........ 6.318680e+03
var 8:
best............ 4.863729e+01
mean............ 7.411473e+01
variance........ 7.668376e+03
var 9:
best............ 1.650151e+02
mean............ 1.786233e+02
variance........ 4.022642e+03

GENERATION: 18
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  5.092679e-01  5.643207e-01  7.060257e-01  7.394108e-01  7.394108e-01  8.046187e-01  8.070437e-01  8.941820e-01  9.999999e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 349, #Total UniqueCount: 6731
var 1:
best............ 8.069966e+02
mean............ 8.216425e+02
variance........ 7.666296e+03
var 2:
best............ 2.803131e+02
mean............ 2.880245e+02
variance........ 4.099666e+03
var 3:
best............ 2.513668e+02
mean............ 2.597484e+02
variance........ 2.773212e+03
var 4:
best............ 6.805006e+02
mean............ 6.973224e+02
variance........ 4.693627e+03
var 5:
best............ 9.105738e+02
mean............ 8.925828e+02
variance........ 8.821605e+03
var 6:
best............ 5.191253e+02
mean............ 5.114017e+02
variance........ 2.876269e+03
var 7:
best............ 2.091762e+01
mean............ 4.691459e+01
variance........ 1.024678e+04
var 8:
best............ 4.863729e+01
mean............ 6.801647e+01
variance........ 6.949746e+03
var 9:
best............ 1.650151e+02
mean............ 1.847263e+02
variance........ 7.729411e+03

GENERATION: 19
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  5.092679e-01  5.643207e-01  7.060257e-01  7.394108e-01  7.394108e-01  8.046187e-01  8.070437e-01  8.941820e-01  9.999999e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 334, #Total UniqueCount: 7065
var 1:
best............ 8.069966e+02
mean............ 8.139740e+02
variance........ 1.004911e+04
var 2:
best............ 2.803131e+02
mean............ 2.881050e+02
variance........ 2.263054e+03
var 3:
best............ 2.513668e+02
mean............ 2.638687e+02
variance........ 5.300330e+03
var 4:
best............ 6.805006e+02
mean............ 7.009334e+02
variance........ 2.251080e+03
var 5:
best............ 9.105738e+02
mean............ 8.932668e+02
variance........ 9.600240e+03
var 6:
best............ 5.191253e+02
mean............ 5.198936e+02
variance........ 1.601972e+03
var 7:
best............ 2.091762e+01
mean............ 4.102468e+01
variance........ 6.690554e+03
var 8:
best............ 4.863729e+01
mean............ 6.658016e+01
variance........ 5.619685e+03
var 9:
best............ 1.650151e+02
mean............ 1.754872e+02
variance........ 3.804818e+03

GENERATION: 20
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  5.092679e-01  5.643207e-01  7.060257e-01  7.394108e-01  7.394108e-01  8.046187e-01  8.070437e-01  8.941820e-01  9.999999e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 330, #Total UniqueCount: 7395
var 1:
best............ 8.069966e+02
mean............ 8.203677e+02
variance........ 6.423628e+03
var 2:
best............ 2.803131e+02
mean............ 2.873879e+02
variance........ 2.670719e+03
var 3:
best............ 2.513668e+02
mean............ 2.669037e+02
variance........ 3.782873e+03
var 4:
best............ 6.805006e+02
mean............ 6.950175e+02
variance........ 3.867043e+03
var 5:
best............ 9.105738e+02
mean............ 8.972352e+02
variance........ 7.177969e+03
var 6:
best............ 5.191253e+02
mean............ 5.127598e+02
variance........ 3.905008e+03
var 7:
best............ 2.091762e+01
mean............ 3.504329e+01
variance........ 2.716306e+03
var 8:
best............ 4.863729e+01
mean............ 6.532225e+01
variance........ 4.357758e+03
var 9:
best............ 1.650151e+02
mean............ 1.796067e+02
variance........ 4.614532e+03

GENERATION: 21
Lexical Fit..... 3.173265e-01  3.173265e-01  3.173265e-01  5.092679e-01  5.643207e-01  7.060257e-01  7.394108e-01  7.394108e-01  8.046187e-01  8.070437e-01  8.941820e-01  9.999999e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 330, #Total UniqueCount: 7725
var 1:
best............ 8.069966e+02
mean............ 8.272744e+02
variance........ 5.697013e+03
var 2:
best............ 2.803131e+02
mean............ 2.915091e+02
variance........ 4.738097e+03
var 3:
best............ 2.513668e+02
mean............ 2.630209e+02
variance........ 4.288892e+03
var 4:
best............ 6.805006e+02
mean............ 7.030699e+02
variance........ 2.111904e+03
var 5:
best............ 9.105738e+02
mean............ 8.897287e+02
variance........ 1.147852e+04
var 6:
best............ 5.191253e+02
mean............ 5.158944e+02
variance........ 2.530679e+03
var 7:
best............ 2.091762e+01
mean............ 3.233819e+01
variance........ 3.457838e+03
var 8:
best............ 4.863729e+01
mean............ 7.927742e+01
variance........ 1.496690e+04
var 9:
best............ 1.650151e+02
mean............ 1.822273e+02
variance........ 5.379073e+03

'wait.generations' limit reached.
No significant improvement in 4 generations.

Solution Lexical Fitness Value:
3.173265e-01  3.173265e-01  3.173265e-01  5.092679e-01  5.643207e-01  7.060257e-01  7.394108e-01  7.394108e-01  8.046187e-01  8.070437e-01  8.941820e-01  9.999999e-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] :    8.069966e+02
 X[ 2] :    2.803131e+02
 X[ 3] :    2.513668e+02
 X[ 4] :    6.805006e+02
 X[ 5] :    9.105738e+02
 X[ 6] :    5.191253e+02
 X[ 7] :    2.091762e+01
 X[ 8] :    4.863729e+01
 X[ 9] :    1.650151e+02

Solution Found Generation 16
Number of Generations Run 21

Mon Jun 19 13:45:41 2017
Total run time : 0 hours 1 minutes and 1 seconds
genoutTT <- GenMatch(Tr=stopsubt1t2$treat, X=XTT, int.seed = 42, unif.seed = 43,
                     BalanceMatrix=BalmatTT, estimand="ATT",
                     pop.size=500)


Mon Jun 19 13:45:41 2017
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 

Data Type: Floating Point
Operators (code number, name, population) 
    (1) Cloning...........................  65
    (2) Uniform Mutation..................  62
    (3) Boundary Mutation.................  62
    (4) Non-Uniform Mutation..............  62
    (5) Polytope Crossover................  62
    (6) Simple Crossover..................  62
    (7) Whole Non-Uniform Mutation........  62
    (8) Heuristic Crossover...............  62
    (9) Local-Minimum Crossover...........  0

SOFT Maximum Number of Generations: 100
Maximum Nonchanging Generations: 4
Population size       : 500
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..... 5.609048e-02  1.079615e-01  1.556071e-01  1.608829e-01  1.830738e-01  2.563231e-01  3.173618e-01  3.173618e-01  8.299950e-01  8.299950e-01  9.999741e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 500, #Total UniqueCount: 500
var 1:
best............ 3.582475e+02
mean............ 5.171244e+02
variance........ 7.903943e+04
var 2:
best............ 7.373775e+02
mean............ 5.081839e+02
variance........ 8.396387e+04
var 3:
best............ 2.953135e+02
mean............ 4.937976e+02
variance........ 8.388562e+04
var 4:
best............ 4.296259e+02
mean............ 5.144284e+02
variance........ 9.014212e+04
var 5:
best............ 4.322205e+01
mean............ 4.980008e+02
variance........ 8.145723e+04
var 6:
best............ 9.376336e+02
mean............ 5.023210e+02
variance........ 8.209425e+04
var 7:
best............ 4.076013e+01
mean............ 5.068778e+02
variance........ 8.157006e+04
var 8:
best............ 2.584087e+02
mean............ 5.026291e+02
variance........ 7.858707e+04
var 9:
best............ 3.070833e+02
mean............ 4.872606e+02
variance........ 8.669141e+04

GENERATION: 1
Lexical Fit..... 5.609048e-02  1.556071e-01  1.752565e-01  2.477867e-01  2.636580e-01  3.173618e-01  3.173618e-01  3.173618e-01  8.243935e-01  8.243935e-01  9.978474e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 374, #Total UniqueCount: 874
var 1:
best............ 3.582475e+02
mean............ 4.645806e+02
variance........ 4.754810e+04
var 2:
best............ 7.373775e+02
mean............ 6.053939e+02
variance........ 4.540005e+04
var 3:
best............ 2.953135e+02
mean............ 4.520580e+02
variance........ 5.307963e+04
var 4:
best............ 6.456318e+02
mean............ 4.334645e+02
variance........ 5.290685e+04
var 5:
best............ 3.758987e+01
mean............ 2.482670e+02
variance........ 8.384201e+04
var 6:
best............ 9.376336e+02
mean............ 7.650527e+02
variance........ 5.177652e+04
var 7:
best............ 4.076013e+01
mean............ 1.722731e+02
variance........ 5.979429e+04
var 8:
best............ 2.610480e+02
mean............ 3.078526e+02
variance........ 2.496748e+04
var 9:
best............ 3.070833e+02
mean............ 4.127446e+02
variance........ 5.261949e+04

GENERATION: 2
Lexical Fit..... 5.609048e-02  1.556071e-01  1.771629e-01  3.173618e-01  3.173618e-01  3.173618e-01  3.364049e-01  4.242517e-01  8.243935e-01  8.243935e-01  9.978474e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 355, #Total UniqueCount: 1229
var 1:
best............ 3.582475e+02
mean............ 3.878549e+02
variance........ 1.045562e+04
var 2:
best............ 7.373775e+02
mean............ 6.718215e+02
variance........ 1.814980e+04
var 3:
best............ 2.953135e+02
mean............ 3.975927e+02
variance........ 3.127666e+04
var 4:
best............ 8.294777e+02
mean............ 4.948313e+02
variance........ 3.872868e+04
var 5:
best............ 4.322205e+01
mean............ 7.892210e+01
variance........ 1.125810e+04
var 6:
best............ 9.376336e+02
mean............ 8.845348e+02
variance........ 1.239961e+04
var 7:
best............ 4.076013e+01
mean............ 6.426989e+01
variance........ 9.744765e+03
var 8:
best............ 2.584087e+02
mean............ 2.690444e+02
variance........ 8.349294e+03
var 9:
best............ 4.858072e+02
mean............ 3.768775e+02
variance........ 1.593518e+04

GENERATION: 3
Lexical Fit..... 5.609048e-02  1.879669e-01  2.048151e-01  2.048151e-01  3.173618e-01  3.173618e-01  3.752161e-01  4.439460e-01  8.243935e-01  8.243935e-01  9.999700e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 371, #Total UniqueCount: 1600
var 1:
best............ 3.582475e+02
mean............ 3.775347e+02
variance........ 7.819193e+03
var 2:
best............ 5.670602e+02
mean............ 7.099443e+02
variance........ 8.349067e+03
var 3:
best............ 2.953135e+02
mean............ 3.245238e+02
variance........ 9.600205e+03
var 4:
best............ 8.294777e+02
mean............ 6.912720e+02
variance........ 2.482095e+04
var 5:
best............ 4.322205e+01
mean............ 6.518041e+01
variance........ 7.207449e+03
var 6:
best............ 9.376336e+02
mean............ 9.053370e+02
variance........ 1.138359e+04
var 7:
best............ 4.076013e+01
mean............ 6.845565e+01
variance........ 1.032525e+04
var 8:
best............ 3.357539e+02
mean............ 2.755545e+02
variance........ 6.266387e+03
var 9:
best............ 4.858072e+02
mean............ 4.603011e+02
variance........ 7.504840e+03

GENERATION: 4
Lexical Fit..... 5.609048e-02  2.048151e-01  2.048151e-01  2.058317e-01  3.173618e-01  3.173618e-01  3.364049e-01  3.387946e-01  8.243935e-01  8.243935e-01  9.999700e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 360, #Total UniqueCount: 1960
var 1:
best............ 3.582475e+02
mean............ 4.138270e+02
variance........ 2.253147e+04
var 2:
best............ 5.670602e+02
mean............ 6.194530e+02
variance........ 1.510995e+04
var 3:
best............ 2.953135e+02
mean............ 3.280249e+02
variance........ 8.780559e+03
var 4:
best............ 8.294777e+02
mean............ 7.865691e+02
variance........ 1.078129e+04
var 5:
best............ 4.322205e+01
mean............ 8.048528e+01
variance........ 1.246606e+04
var 6:
best............ 9.376336e+02
mean............ 9.075851e+02
variance........ 1.166798e+04
var 7:
best............ 4.076013e+01
mean............ 7.311540e+01
variance........ 1.335137e+04
var 8:
best............ 3.357539e+02
mean............ 3.876730e+02
variance........ 2.593824e+04
var 9:
best............ 3.858704e+02
mean............ 4.694861e+02
variance........ 8.440212e+03

GENERATION: 5
Lexical Fit..... 5.609048e-02  2.048151e-01  2.048151e-01  2.058317e-01  3.173618e-01  3.173618e-01  3.364049e-01  3.387946e-01  8.243935e-01  8.243935e-01  9.999700e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 355, #Total UniqueCount: 2315
var 1:
best............ 3.582475e+02
mean............ 3.855121e+02
variance........ 1.068328e+04
var 2:
best............ 5.670602e+02
mean............ 5.749979e+02
variance........ 7.437318e+03
var 3:
best............ 2.953135e+02
mean............ 3.142885e+02
variance........ 5.897752e+03
var 4:
best............ 8.294777e+02
mean............ 7.970412e+02
variance........ 1.298573e+04
var 5:
best............ 4.322205e+01
mean............ 8.160525e+01
variance........ 1.355429e+04
var 6:
best............ 9.376336e+02
mean............ 9.011370e+02
variance........ 1.302647e+04
var 7:
best............ 4.076013e+01
mean............ 7.257135e+01
variance........ 1.355707e+04
var 8:
best............ 3.357539e+02
mean............ 3.857885e+02
variance........ 1.492204e+04
var 9:
best............ 3.858704e+02
mean............ 4.092972e+02
variance........ 7.461633e+03

GENERATION: 6
Lexical Fit..... 5.609048e-02  2.048151e-01  2.048151e-01  2.058317e-01  3.173618e-01  3.173618e-01  3.364049e-01  3.387946e-01  8.243935e-01  8.243935e-01  9.999700e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 208, #Total UniqueCount: 2523
var 1:
best............ 3.582475e+02
mean............ 3.698865e+02
variance........ 8.249497e+03
var 2:
best............ 5.670602e+02
mean............ 5.652188e+02
variance........ 5.466846e+03
var 3:
best............ 2.953135e+02
mean............ 3.042529e+02
variance........ 4.471907e+03
var 4:
best............ 8.294777e+02
mean............ 8.132302e+02
variance........ 7.529581e+03
var 5:
best............ 4.322205e+01
mean............ 7.592395e+01
variance........ 1.219717e+04
var 6:
best............ 9.376336e+02
mean............ 9.128875e+02
variance........ 7.940989e+03
var 7:
best............ 4.076013e+01
mean............ 6.797080e+01
variance........ 1.143662e+04
var 8:
best............ 3.357539e+02
mean............ 3.515534e+02
variance........ 7.556374e+03
var 9:
best............ 3.858704e+02
mean............ 4.034161e+02
variance........ 5.252559e+03

GENERATION: 7
Lexical Fit..... 5.609048e-02  2.048151e-01  2.048151e-01  2.058317e-01  3.173618e-01  3.173618e-01  3.364049e-01  3.387946e-01  8.243935e-01  8.243935e-01  9.999700e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 210, #Total UniqueCount: 2733
var 1:
best............ 3.582475e+02
mean............ 3.692002e+02
variance........ 6.082402e+03
var 2:
best............ 5.670602e+02
mean............ 5.608275e+02
variance........ 6.085554e+03
var 3:
best............ 2.953135e+02
mean............ 3.090302e+02
variance........ 6.625756e+03
var 4:
best............ 8.294777e+02
mean............ 8.109653e+02
variance........ 7.090968e+03
var 5:
best............ 4.322205e+01
mean............ 6.556456e+01
variance........ 8.306926e+03
var 6:
best............ 9.376336e+02
mean............ 9.136203e+02
variance........ 8.696157e+03
var 7:
best............ 4.076013e+01
mean............ 6.742253e+01
variance........ 1.036569e+04
var 8:
best............ 3.357539e+02
mean............ 3.450969e+02
variance........ 5.918547e+03
var 9:
best............ 3.858704e+02
mean............ 3.913021e+02
variance........ 3.543333e+03

GENERATION: 8
Lexical Fit..... 5.609048e-02  2.048151e-01  2.048151e-01  2.081699e-01  3.173618e-01  3.173618e-01  4.439460e-01  5.531007e-01  8.243935e-01  8.243935e-01  9.999700e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 217, #Total UniqueCount: 2950
var 1:
best............ 3.582475e+02
mean............ 3.666525e+02
variance........ 6.907917e+03
var 2:
best............ 5.670602e+02
mean............ 5.670256e+02
variance........ 4.106266e+03
var 3:
best............ 2.953135e+02
mean............ 3.113085e+02
variance........ 5.253201e+03
var 4:
best............ 8.294777e+02
mean............ 8.127928e+02
variance........ 6.388840e+03
var 5:
best............ 6.401575e+01
mean............ 6.332002e+01
variance........ 8.232132e+03
var 6:
best............ 9.376336e+02
mean............ 9.187853e+02
variance........ 6.078652e+03
var 7:
best............ 4.076013e+01
mean............ 6.038347e+01
variance........ 7.831146e+03
var 8:
best............ 3.357539e+02
mean............ 3.485800e+02
variance........ 6.115878e+03
var 9:
best............ 5.799323e+02
mean............ 3.867777e+02
variance........ 3.451345e+03

GENERATION: 9
Lexical Fit..... 5.609048e-02  2.048151e-01  2.048151e-01  2.081699e-01  3.173618e-01  3.173618e-01  4.439460e-01  5.531007e-01  8.243935e-01  8.243935e-01  9.999700e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 353, #Total UniqueCount: 3303
var 1:
best............ 3.582475e+02
mean............ 3.724821e+02
variance........ 5.878241e+03
var 2:
best............ 5.670602e+02
mean............ 5.616822e+02
variance........ 5.474940e+03
var 3:
best............ 2.953135e+02
mean............ 2.991814e+02
variance........ 4.198196e+03
var 4:
best............ 8.294777e+02
mean............ 8.161104e+02
variance........ 5.067267e+03
var 5:
best............ 6.401575e+01
mean............ 7.411537e+01
variance........ 7.387934e+03
var 6:
best............ 9.376336e+02
mean............ 9.090173e+02
variance........ 8.875296e+03
var 7:
best............ 4.076013e+01
mean............ 6.384553e+01
variance........ 8.469137e+03
var 8:
best............ 3.357539e+02
mean............ 3.433516e+02
variance........ 5.591500e+03
var 9:
best............ 5.799323e+02
mean............ 4.746145e+02
variance........ 1.434880e+04

GENERATION: 10
Lexical Fit..... 5.609048e-02  2.048151e-01  2.048151e-01  2.081699e-01  3.173618e-01  3.173618e-01  4.439460e-01  5.531007e-01  8.243935e-01  8.243935e-01  9.999700e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 325, #Total UniqueCount: 3628
var 1:
best............ 3.582475e+02
mean............ 3.839088e+02
variance........ 6.629270e+03
var 2:
best............ 5.670602e+02
mean............ 5.870066e+02
variance........ 6.640555e+03
var 3:
best............ 2.953135e+02
mean............ 3.080636e+02
variance........ 5.454388e+03
var 4:
best............ 8.294777e+02
mean............ 8.158052e+02
variance........ 5.947572e+03
var 5:
best............ 6.401575e+01
mean............ 7.938849e+01
variance........ 7.259062e+03
var 6:
best............ 9.376336e+02
mean............ 9.080563e+02
variance........ 1.249748e+04
var 7:
best............ 4.076013e+01
mean............ 6.066748e+01
variance........ 6.611780e+03
var 8:
best............ 3.357539e+02
mean............ 3.472973e+02
variance........ 4.799683e+03
var 9:
best............ 5.799323e+02
mean............ 5.434278e+02
variance........ 8.938780e+03

GENERATION: 11
Lexical Fit..... 5.609048e-02  2.048151e-01  2.048151e-01  2.081699e-01  3.173618e-01  3.173618e-01  4.439460e-01  5.531007e-01  8.243935e-01  8.243935e-01  9.999700e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 329, #Total UniqueCount: 3957
var 1:
best............ 3.582475e+02
mean............ 3.795031e+02
variance........ 4.931484e+03
var 2:
best............ 5.670602e+02
mean............ 5.877699e+02
variance........ 5.782380e+03
var 3:
best............ 2.953135e+02
mean............ 3.040950e+02
variance........ 4.663600e+03
var 4:
best............ 8.294777e+02
mean............ 8.112617e+02
variance........ 7.863970e+03
var 5:
best............ 6.401575e+01
mean............ 9.398437e+01
variance........ 1.291156e+04
var 6:
best............ 9.376336e+02
mean............ 9.154189e+02
variance........ 8.042415e+03
var 7:
best............ 4.076013e+01
mean............ 6.754593e+01
variance........ 8.953824e+03
var 8:
best............ 3.357539e+02
mean............ 3.481096e+02
variance........ 5.625337e+03
var 9:
best............ 5.799323e+02
mean............ 5.767687e+02
variance........ 4.392595e+03

GENERATION: 12
Lexical Fit..... 5.609048e-02  2.048151e-01  2.048151e-01  2.081699e-01  3.173618e-01  3.173618e-01  4.439460e-01  5.531007e-01  8.243935e-01  8.243935e-01  9.999700e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 317, #Total UniqueCount: 4274
var 1:
best............ 3.582475e+02
mean............ 3.819457e+02
variance........ 3.994445e+03
var 2:
best............ 5.670602e+02
mean............ 5.927323e+02
variance........ 6.044363e+03
var 3:
best............ 2.953135e+02
mean............ 3.053719e+02
variance........ 5.075271e+03
var 4:
best............ 8.294777e+02
mean............ 8.203061e+02
variance........ 5.778553e+03
var 5:
best............ 6.401575e+01
mean............ 8.345737e+01
variance........ 7.457225e+03
var 6:
best............ 9.376336e+02
mean............ 9.188495e+02
variance........ 4.222432e+03
var 7:
best............ 4.076013e+01
mean............ 5.551264e+01
variance........ 3.593630e+03
var 8:
best............ 3.357539e+02
mean............ 3.484774e+02
variance........ 4.961935e+03
var 9:
best............ 5.799323e+02
mean............ 5.755939e+02
variance........ 4.838838e+03

GENERATION: 13
Lexical Fit..... 5.609048e-02  2.048151e-01  2.048151e-01  2.081699e-01  3.173618e-01  3.173618e-01  4.439460e-01  5.531007e-01  8.243935e-01  8.243935e-01  9.999700e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 313, #Total UniqueCount: 4587
var 1:
best............ 3.582475e+02
mean............ 3.770857e+02
variance........ 3.533597e+03
var 2:
best............ 5.670602e+02
mean............ 5.840944e+02
variance........ 6.232233e+03
var 3:
best............ 2.953135e+02
mean............ 3.042805e+02
variance........ 3.293092e+03
var 4:
best............ 8.294777e+02
mean............ 8.191139e+02
variance........ 5.662275e+03
var 5:
best............ 6.401575e+01
mean............ 7.406380e+01
variance........ 3.486868e+03
var 6:
best............ 9.376336e+02
mean............ 9.174427e+02
variance........ 8.829328e+03
var 7:
best............ 4.076013e+01
mean............ 5.685661e+01
variance........ 6.260329e+03
var 8:
best............ 3.357539e+02
mean............ 3.396506e+02
variance........ 1.496478e+03
var 9:
best............ 5.799323e+02
mean............ 5.762354e+02
variance........ 2.696272e+03

'wait.generations' limit reached.
No significant improvement in 4 generations.

Solution Lexical Fitness Value:
5.609048e-02  2.048151e-01  2.048151e-01  2.081699e-01  3.173618e-01  3.173618e-01  4.439460e-01  5.531007e-01  8.243935e-01  8.243935e-01  9.999700e-01  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] :    3.582475e+02
 X[ 2] :    5.670602e+02
 X[ 3] :    2.953135e+02
 X[ 4] :    8.294777e+02
 X[ 5] :    6.401575e+01
 X[ 6] :    9.376336e+02
 X[ 7] :    4.076013e+01
 X[ 8] :    3.357539e+02
 X[ 9] :    5.799323e+02

Solution Found Generation 8
Number of Generations Run 13

Mon Jun 19 13:46:18 2017
Total run time : 0 hours 0 minutes and 37 seconds

Match balance is tested for below in the wave 1 treatment and control group observations.

mb1  <- MatchBalance(stopsubt1c1$treat ~stopsubt1c1$e1 + stopsubt1c1$e2 + stopsubt1c1$e5 + stopsubt1c1$e6 + stopsubt1c1$e7 + stopsubt1c1$e8 + stopsubt1c1$e9 + stopsubt1c1$distance + stopsubt1c1$psm , match.out=mout1, nboots=5000)

***** (V1) stopsubt1c1$e1 *****
                       Before Matching       After Matching
mean treatment........    0.74757           0.74757 
mean control..........    0.80882           0.72816 
std mean diff.........    -12.778            4.0508 

mean raw eQQ diff.....   0.073529          0.018519 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........   0.020417         0.0061728 
med  eCDF diff........  0.0047116         0.0092593 
max  eCDF diff........   0.056539         0.0092593 

var ratio (Tr/Co).....     1.2301           0.88815 
T-test p-value........    0.38666           0.56435 
KS Bootstrap p-value..     0.3764             0.987 
KS Naive p-value......    0.99944                 1 
KS Statistic..........   0.056539         0.0092593 


***** (V2) stopsubt1c1$e2 *****
                       Before Matching       After Matching
mean treatment........    0.52427           0.52427 
mean control..........    0.33824           0.50485 
std mean diff.........     24.319            2.5383 

mean raw eQQ diff.....    0.20588          0.037037 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........   0.051364         0.0092593 
med  eCDF diff........   0.042333         0.0092593 
max  eCDF diff........    0.12079          0.018519 

var ratio (Tr/Co).....    0.86703           0.99903 
T-test p-value........    0.13876           0.56435 
KS Bootstrap p-value..     0.1312            0.9574 
KS Naive p-value......    0.58857                 1 
KS Statistic..........    0.12079          0.018519 


***** (V3) stopsubt1c1$e5 *****
                       Before Matching       After Matching
mean treatment........    0.24272           0.24272 
mean control..........       0.25           0.26214 
std mean diff.........    -1.6074           -4.2863 

mean raw eQQ diff.....   0.014706          0.018519 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........  0.0040453         0.0061728 
med  eCDF diff........  0.0024272         0.0092593 
max  eCDF diff........  0.0097087         0.0092593 

var ratio (Tr/Co).....     1.0784            1.0507 
T-test p-value........    0.91636           0.61773 
KS Bootstrap p-value..     0.9464             0.971 
KS Naive p-value......          1                 1 
KS Statistic..........  0.0097087         0.0092593 


***** (V4) stopsubt1c1$e6 *****
                       Before Matching       After Matching
mean treatment........    0.86408           0.86408 
mean control..........    0.83824           0.87379 
std mean diff.........     5.8219           -2.1872 

mean raw eQQ diff.....   0.029412         0.0092593 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........  0.0088164         0.0023148 
med  eCDF diff........  0.0048544                 0 
max  eCDF diff........   0.025557         0.0092593 

var ratio (Tr/Co).....    0.86731            1.1577 
T-test p-value........    0.72199           0.31733 
KS Bootstrap p-value..      0.692             0.986 
KS Naive p-value......          1                 1 
KS Statistic..........   0.025557         0.0092593 


***** (V5) stopsubt1c1$e7 *****
                       Before Matching       After Matching
mean treatment........ -0.0097087        -0.0097087 
mean control..........   0.044118        -0.0097087 
std mean diff.........    -16.398                 0 

mean raw eQQ diff.....   0.073529                 0 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          2                 0 

mean eCDF diff........   0.016372                 0 
med  eCDF diff........  0.0047116                 0 
max  eCDF diff........   0.044403                 0 

var ratio (Tr/Co).....     0.8141                 1 
T-test p-value........    0.32692                 1 
KS Bootstrap p-value..     0.2102                 1 
KS Naive p-value......          1                 1 
KS Statistic..........   0.044403                 0 


***** (V6) stopsubt1c1$e8 *****
                       Before Matching       After Matching
mean treatment........    0.91262           0.91262 
mean control..........    0.88235           0.93204 
std mean diff.........     7.5919           -4.8703 

mean raw eQQ diff.....    0.10294          0.037037 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          2                 2 

mean eCDF diff........    0.02213         0.0092593 
med  eCDF diff........   0.014563         0.0092593 
max  eCDF diff........   0.059395          0.018519 

var ratio (Tr/Co).....     1.5088            2.4851 
T-test p-value........      0.587            0.5277 
KS Bootstrap p-value..     0.1658            0.7448 
KS Naive p-value......    0.99871                 1 
KS Statistic..........   0.059395          0.018519 


***** (V7) stopsubt1c1$e9 *****
                       Before Matching       After Matching
mean treatment........ -0.0097087        -0.0097087 
mean control..........   0.073529                 0 
std mean diff.........    -84.477           -9.8533 

mean raw eQQ diff.....   0.088235         0.0092593 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........   0.027746         0.0046296 
med  eCDF diff........  0.0097087         0.0046296 
max  eCDF diff........   0.073529         0.0092593 

var ratio (Tr/Co).....    0.14042               Inf 
T-test p-value........    0.01458           0.31733 
KS Bootstrap p-value..     0.0068            0.5224 
KS Naive p-value......    0.97972                 1 
KS Statistic..........   0.073529         0.0092593 


***** (V8) stopsubt1c1$distance *****
                       Before Matching       After Matching
mean treatment........     129.27            129.27 
mean control..........     123.69            127.16 
std mean diff.........     7.7599             2.943 

mean raw eQQ diff.....     7.6912            8.5093 
med  raw eQQ diff.....          5               4.5 
max  raw eQQ diff.....         32                53 

mean eCDF diff........   0.036542          0.027199 
med  eCDF diff........   0.032125          0.027778 
max  eCDF diff........    0.10908          0.074074 

var ratio (Tr/Co).....    0.89971            1.2404 
T-test p-value........    0.63145           0.67945 
KS Bootstrap p-value..     0.5596            0.8432 
KS Naive p-value......    0.71438            0.9284 
KS Statistic..........    0.10908          0.074074 


***** (V9) stopsubt1c1$psm *****
                       Before Matching       After Matching
mean treatment........    0.63825           0.63825 
mean control..........    0.54794            0.6345 
std mean diff.........     84.228            3.4977 

mean raw eQQ diff.....   0.088451          0.012034 
med  raw eQQ diff.....   0.062824         0.0077682 
max  raw eQQ diff.....    0.45146           0.13557 

mean eCDF diff........    0.16226          0.027948 
med  eCDF diff........    0.16826          0.027778 
max  eCDF diff........    0.30226          0.083333 

var ratio (Tr/Co).....    0.35166             1.218 
T-test p-value........ 0.00034284           0.49358 
KS Bootstrap p-value..     0.0012            0.7962 
KS Naive p-value......  0.0011241           0.84749 
KS Statistic..........    0.30226          0.083333 


Before Matching Minimum p.value: 0.00034284 
Variable Name(s): stopsubt1c1$psm  Number(s): 9 

After Matching Minimum p.value: 0.31733 
Variable Name(s): stopsubt1c1$e6 stopsubt1c1$e9  Number(s): 4 7 

Here match balance is presented for the comparison of the treatment wave one observations as well as treatment wave two observations.

mout2<-Match(Tr=stopsubt1t2$treat, X=XTT, estimand="ATT", 
             Weight.matrix=genoutTT)
mb2  <- MatchBalance(stopsubt1t2$treat ~stopsubt1t2$e1 + stopsubt1t2$e2 + stopsubt1t2$e5 + stopsubt1t2$e6 + stopsubt1t2$e7 + stopsubt1t2$e8 + stopsubt1t2$e9 + stopsubt1t2$distance + stopsubt1t2$psm , match.out=mout2, nboots=5000)

***** (V1) stopsubt1t2$e1 *****
                       Before Matching       After Matching
mean treatment........    0.66667           0.66667 
mean control..........    0.74757              0.65 
std mean diff.........    -17.019            3.5059 

mean raw eQQ diff.....    0.13333          0.012346 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........   0.039914         0.0061728 
med  eCDF diff........   0.019417         0.0061728 
max  eCDF diff........    0.10032          0.012346 

var ratio (Tr/Co).....    0.98353            0.9768 
T-test p-value........    0.29817           0.31736 
KS Bootstrap p-value..     0.1662            0.9296 
KS Naive p-value......    0.83996                 1 
KS Statistic..........    0.10032          0.012346 


***** (V2) stopsubt1t2$e2 *****
                       Before Matching       After Matching
mean treatment........    0.26667           0.26667 
mean control..........    0.52427               0.2 
std mean diff.........    -30.629            7.9267 

mean raw eQQ diff.....    0.23333          0.074074 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........   0.064401          0.018519 
med  eCDF diff........   0.038997          0.012346 
max  eCDF diff........    0.17961          0.049383 

var ratio (Tr/Co).....     1.2087           0.95719 
T-test p-value........   0.053753           0.20482 
KS Bootstrap p-value..     0.0278             0.765 
KS Naive p-value......    0.17312           0.99997 
KS Statistic..........    0.17961          0.049383 


***** (V3) stopsubt1t2$e5 *****
                       Before Matching       After Matching
mean treatment........    0.38333           0.38333 
mean control..........    0.24272              0.45 
std mean diff.........     26.849           -12.729 

mean raw eQQ diff.....       0.15          0.049383 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........    0.05151          0.016461 
med  eCDF diff........  0.0069579          0.012346 
max  eCDF diff........    0.14757          0.037037 

var ratio (Tr/Co).....     1.3366            1.0898 
T-test p-value........   0.085446           0.20482 
KS Bootstrap p-value..     0.0498             0.692 
KS Naive p-value......    0.38087                 1 
KS Statistic..........    0.14757          0.037037 


***** (V4) stopsubt1t2$e6 *****
                       Before Matching       After Matching
mean treatment........    0.86667           0.86667 
mean control..........    0.86408           0.88333 
std mean diff.........    0.55297           -3.5598 

mean raw eQQ diff.....   0.033333          0.012346 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........  0.0041262         0.0041152 
med  eCDF diff........  0.0047735                 0 
max  eCDF diff........  0.0069579          0.012346 

var ratio (Tr/Co).....     1.1126            1.0616 
T-test p-value........    0.97238           0.31736 
KS Bootstrap p-value..      0.985            0.9546 
KS Naive p-value......          1                 1 
KS Statistic..........  0.0069579          0.012346 


***** (V5) stopsubt1t2$e7 *****
                       Before Matching       After Matching
mean treatment........  -0.033333         -0.033333 
mean control.......... -0.0097087         -0.033333 
std mean diff.........    -5.7576                 0 

mean raw eQQ diff.....          0          0.049383 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          0                 2 

mean eCDF diff........   0.006041          0.012346 
med  eCDF diff........  0.0042071          0.012346 
max  eCDF diff........   0.013916          0.024691 

var ratio (Tr/Co).....     1.5626            2.5254 
T-test p-value........    0.70426                 1 
KS Bootstrap p-value..       0.85             0.509 
KS Naive p-value......          1                 1 
KS Statistic..........   0.013916          0.024691 


***** (V6) stopsubt1t2$e8 *****
                       Before Matching       After Matching
mean treatment........       0.85              0.85 
mean control..........    0.91262              0.85 
std mean diff.........    -13.019                 0 

mean raw eQQ diff.....   0.066667                 0 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 0 

mean eCDF diff........   0.017031                 0 
med  eCDF diff........  0.0048544                 0 
max  eCDF diff........   0.058414                 0 

var ratio (Tr/Co).....     1.4555                 1 
T-test p-value........    0.39601                 1 
KS Bootstrap p-value..     0.1922                 1 
KS Naive p-value......     0.9995                 1 
KS Statistic..........   0.058414                 0 


***** (V7) stopsubt1t2$e9 *****
                       Before Matching       After Matching
mean treatment........   0.083333          0.083333 
mean control.......... -0.0097087                 0 
std mean diff.........     27.854            24.947 

mean raw eQQ diff.....        0.1           0.11111 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........   0.035653          0.037037 
med  eCDF diff........  0.0069579          0.012346 
max  eCDF diff........        0.1          0.098765 

var ratio (Tr/Co).....     11.493               Inf 
T-test p-value........   0.039172           0.05609 
KS Bootstrap p-value..      0.002            0.0064 
KS Naive p-value......    0.84277           0.82439 
KS Statistic..........        0.1          0.098765 


***** (V8) stopsubt1t2$distance *****
                       Before Matching       After Matching
mean treatment........     120.55            120.55 
mean control..........     129.27            116.95 
std mean diff.........    -13.219            5.4561 

mean raw eQQ diff.....     8.7167            11.654 
med  raw eQQ diff.....          5                10 
max  raw eQQ diff.....         48                53 

mean eCDF diff........   0.031383          0.052967 
med  eCDF diff........   0.034628          0.037037 
max  eCDF diff........   0.072977            0.1358 

var ratio (Tr/Co).....    0.84174           0.72581 
T-test p-value........     0.4326            0.5531 
KS Bootstrap p-value..     0.9136            0.3146 
KS Naive p-value......    0.98761           0.44395 
KS Statistic..........   0.072977            0.1358 


***** (V9) stopsubt1t2$psm *****
                       Before Matching       After Matching
mean treatment........    0.43269           0.43269 
mean control..........    0.33047            0.4112 
std mean diff.........      54.61            11.481 

mean raw eQQ diff.....    0.10502          0.038084 
med  raw eQQ diff.....    0.10001         0.0076586 
max  raw eQQ diff.....    0.32858           0.31456 

mean eCDF diff........    0.17807          0.030214 
med  eCDF diff........    0.18697          0.024691 
max  eCDF diff........    0.31845          0.098765 

var ratio (Tr/Co).....     2.5367            2.1783 
T-test p-value........ 0.00025511           0.20817 
KS Bootstrap p-value..      2e-04            0.7546 
KS Naive p-value...... 0.00091512           0.82439 
KS Statistic..........    0.31845          0.098765 


Before Matching Minimum p.value: 2e-04 
Variable Name(s): stopsubt1t2$psm  Number(s): 9 

After Matching Minimum p.value: 0.0064 
Variable Name(s): stopsubt1t2$e9  Number(s): 7 

Finally, the match balance for the comparison of control groups in the first round of observation compared to in the second round of observation.

mout3<-Match(Tr=stopsubc1c2$treat, X=XCC, estimand="ATT", 
             Weight.matrix=genoutCC)
mb3  <- MatchBalance(stopsubc1c2$treat ~stopsubc1c2$e1 + stopsubc1c2$e2 + stopsubc1c2$e5 + stopsubc1c2$e6 + stopsubc1c2$e7 + stopsubc1c2$e8 + stopsubc1c2$e9 + stopsubc1c2$distance + stopsubc1c2$psm , match.out=mout3, nboots=5000)

***** (V1) stopsubc1c2$e1 *****
                       Before Matching       After Matching
mean treatment........    0.84112           0.84112 
mean control..........    0.80882           0.85047 
std mean diff.........     8.7938           -2.5446 

mean raw eQQ diff.....   0.029412         0.0081967 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........   0.010766         0.0040984 
med  eCDF diff........   0.014706         0.0040984 
max  eCDF diff........   0.017592         0.0081967 

var ratio (Tr/Co).....     0.7222            1.0508 
T-test p-value........    0.61081           0.31733 
KS Bootstrap p-value..     0.7944            0.9284 
KS Naive p-value......          1                 1 
KS Statistic..........   0.017592         0.0081967 


***** (V2) stopsubc1c2$e2 *****
                       Before Matching       After Matching
mean treatment........    0.34579           0.34579 
mean control..........    0.33824            0.3271 
std mean diff.........    0.90345             2.234 

mean raw eQQ diff.....   0.029412           0.04918 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........   0.011224          0.016393 
med  eCDF diff........   0.013057          0.016393 
max  eCDF diff........   0.020616          0.032787 

var ratio (Tr/Co).....     1.0372           0.90992 
T-test p-value........    0.95311           0.70603 
KS Bootstrap p-value..     0.9378             0.805 
KS Naive p-value......          1                 1 
KS Statistic..........   0.020616          0.032787 


***** (V3) stopsubc1c2$e5 *****
                       Before Matching       After Matching
mean treatment........    0.19626           0.19626 
mean control..........       0.25           0.20561 
std mean diff.........    -13.467           -2.3421 

mean raw eQQ diff.....   0.058824         0.0081967 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........   0.026869         0.0040984 
med  eCDF diff........   0.026869         0.0040984 
max  eCDF diff........   0.053738         0.0081967 

var ratio (Tr/Co).....    0.83674           0.96578 
T-test p-value........    0.41324           0.73941 


***** (V4) stopsubc1c2$e6 *****
                       Before Matching       After Matching
mean treatment........    0.86916           0.86916 
mean control..........    0.83824            0.8785 
std mean diff.........     7.0894           -2.1426 

mean raw eQQ diff.....   0.044118         0.0081967 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........  0.0097237         0.0020492 
med  eCDF diff........  0.0046729                 0 
max  eCDF diff........   0.029549         0.0081967 

var ratio (Tr/Co).....    0.83754            1.1577 
T-test p-value........    0.66628           0.31733 
KS Bootstrap p-value..     0.5874            0.9862 
KS Naive p-value......          1                 1 
KS Statistic..........   0.029549         0.0081967 


***** (V5) stopsubc1c2$e7 *****
                       Before Matching       After Matching
mean treatment........  0.0093458         0.0093458 
mean control..........   0.044118         0.0093458 
std mean diff.........    -35.968                 0 

mean raw eQQ diff.....   0.088235                 0 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          2                 0 

mean eCDF diff........   0.026296                 0 
med  eCDF diff........   0.014706                 0 
max  eCDF diff........   0.064184                 0 

var ratio (Tr/Co).....   0.070613                 1 
T-test p-value........    0.44316                 1 
KS Bootstrap p-value..     0.0302                 1 
KS Naive p-value......    0.99549                 1 
KS Statistic..........   0.064184                 0 


***** (V6) stopsubc1c2$e8 *****
                       Before Matching       After Matching
mean treatment........    0.84112           0.84112 
mean control..........    0.88235           0.86916 
std mean diff.........    -8.6117           -5.8559 

mean raw eQQ diff.....   0.058824          0.040984 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          2                 2 

mean eCDF diff........   0.010308          0.010246 
med  eCDF diff........   0.006597         0.0081967 
max  eCDF diff........   0.028037           0.02459 

var ratio (Tr/Co).....     2.1758            1.9969 
T-test p-value........     0.4983           0.31733 
KS Bootstrap p-value..     0.6506             0.646 
KS Naive p-value......          1                 1 
KS Statistic..........   0.028037           0.02459 


***** (V7) stopsubc1c2$e9 *****
                       Before Matching       After Matching
mean treatment........   0.018692          0.018692 
mean control..........   0.073529         0.0093458 
std mean diff.........    -28.362            4.8337 

mean raw eQQ diff.....   0.058824           0.02459 
med  raw eQQ diff.....          0                 0 
max  raw eQQ diff.....          1                 1 

mean eCDF diff........   0.018279         0.0081967 
med  eCDF diff........  0.0093458         0.0081967 
max  eCDF diff........   0.045492          0.016393 

var ratio (Tr/Co).....    0.54069                 4 
T-test p-value........    0.14069           0.56432 
KS Bootstrap p-value..     0.1656            0.4906 
KS Naive p-value......    0.99999                 1 
KS Statistic..........   0.045492          0.016393 


***** (V8) stopsubc1c2$distance *****
                       Before Matching       After Matching
mean treatment........     119.57            119.57 
mean control..........     123.69            122.93 
std mean diff.........    -5.0443           -4.1182 

mean raw eQQ diff.....     10.368            7.6639 
med  raw eQQ diff.....        8.5                 3 
max  raw eQQ diff.....         36                32 

mean eCDF diff........   0.034239          0.025894 
med  eCDF diff........   0.035596          0.016393 
max  eCDF diff........   0.064596           0.07377 

var ratio (Tr/Co).....     1.1611            1.1422 
T-test p-value........    0.73433           0.50927 
KS Bootstrap p-value..     0.9586            0.8006 
KS Naive p-value......    0.99509           0.89418 
KS Statistic..........   0.064596           0.07377 


***** (V9) stopsubc1c2$psm *****
                       Before Matching       After Matching
mean treatment........    0.62504           0.62504 
mean control..........    0.59002           0.62365 
std mean diff.........     46.021            1.8245 

mean raw eQQ diff.....   0.032306           0.01143 
med  raw eQQ diff.....  0.0080225         0.0016677 
max  raw eQQ diff.....    0.14997           0.15305 

mean eCDF diff........   0.086226          0.030634 
med  eCDF diff........   0.087479           0.02459 
max  eCDF diff........    0.18059          0.081967 

var ratio (Tr/Co).....    0.55191            1.6167 
T-test p-value........   0.016851           0.80462 
KS Bootstrap p-value..     0.0998            0.7278 
KS Naive p-value......    0.13277           0.80704 
KS Statistic..........    0.18059          0.081967 


Before Matching Minimum p.value: 0.016851 
Variable Name(s): stopsubc1c2$psm  Number(s): 9 

After Matching Minimum p.value: 0.31733 
Variable Name(s): stopsubc1c2$e1 stopsubc1c2$e6 stopsubc1c2$e8  Number(s): 1 4 6 

Below is the code used to estimate the overall treatment effect.

moutTC <- Match(Y=stopsubt1c1$total, Tr=stopsubt1c1$treat, X=XTC, estimand="ATT", 
                Weight.matrix=genoutTC)
summary(moutTC)

Estimate...  -7.2282 
AI SE......  1.7107 
T-stat.....  -4.2252 
p.val......  2.3874e-05 

Original number of observations..............  171 
Original number of treated obs...............  103 
Matched number of observations...............  103 
Matched number of observations  (unweighted).  108 

And the 95% confidence intervals.

tottcconup<-(moutTC$est+moutTC$se*1.96)
tottccondown<-(moutTC$est-moutTC$se*1.96)
tottcinter<-c(tottcconup,tottccondown)
tottcinter
[1]  -3.875125 -10.581186

Estimates for changes in speed.

moutTCspeed <- Match(Y=stopsubt1c1$speed, Tr=stopsubt1c1$treat, X=XTC, estimand="ATT", 
                     Weight.matrix=genoutTC)
summary(moutTCspeed)

Estimate...  -1.406 
AI SE......  3.575 
T-stat.....  -0.39329 
p.val......  0.69411 

Original number of observations..............  171 
Original number of treated obs...............  103 
Matched number of observations...............  103 
Matched number of observations  (unweighted).  108 

And the 95% confidence intervals.

speedtcconup<-(moutTCspeed$est+moutTCspeed$se*1.96)
speedtccondown<-(moutTCspeed$est-moutTCspeed$se*1.96)
speedtcinter<-c(speedtcconup,speedtccondown)
speedtcinter
[1]  5.600969 -8.412971

Estimate of the decline in telephone calls.

moutTCtelephone <- Match(Y=stopsubt1c1$Telephone, Tr=stopsubt1c1$treat, X=XTC, estimand="ATT", 
                         Weight.matrix=genoutTC)
summary(moutTCtelephone)

Estimate...  -1.5049 
AI SE......  0.51659 
T-stat.....  -2.913 
p.val......  0.0035793 

Original number of observations..............  171 
Original number of treated obs...............  103 
Matched number of observations...............  103 
Matched number of observations  (unweighted).  108 

And the 95% confidence intervals.

telephonetcconup<-(moutTCtelephone$est+moutTCtelephone$se*1.96)
telephonetccondown<-(moutTCtelephone$est-moutTCtelephone$se*1.96)
telephonetcinter<-c(telephonetcconup,telephonetccondown)
telephonetcinter
[1] -0.492333 -2.517376

Estimates for effects on texting.

moutTCtexting <- Match(Y=stopsubt1c1$Texting, Tr=stopsubt1c1$treat, X=XTC, estimand="ATT", 
                       Weight.matrix=genoutTC)
summary(moutTCtexting)

Estimate...  0 
AI SE......  0.022086 
T-stat.....  0 
p.val......  1 

Original number of observations..............  171 
Original number of treated obs...............  103 
Matched number of observations...............  103 
Matched number of observations  (unweighted).  108 

And the 95% confidence intervals.

textingtcconup<-(moutTCtexting$est+moutTCtexting$se*1.96)
textingtccondown<-(moutTCtexting$est-moutTCtexting$se*1.96)
textingtcinter<-c(textingtcconup,textingtccondown)
textingtcinter
[1]  0.04328763 -0.04328763

Estimates for the effect on smoking.

moutTCsmoking <- Match(Y=stopsubt1c1$Smoking, Tr=stopsubt1c1$treat, X=XTC, estimand="ATT", 
                       Weight.matrix=genoutTC)
summary(moutTCsmoking)

Estimate...  -1.6019 
AI SE......  0.42616 
T-stat.....  -3.759 
p.val......  0.00017061 

Original number of observations..............  171 
Original number of treated obs...............  103 
Matched number of observations...............  103 
Matched number of observations  (unweighted).  108 

And the 95% confidence intervals.

smokingtcconup<-(moutTCsmoking$est+moutTCsmoking$se*1.96)
smokingtccondown<-(moutTCsmoking$est-moutTCsmoking$se*1.96)
smokingtcinter<-c(smokingtcconup,smokingtccondown)
smokingtcinter
[1] -0.7666589 -2.4372246

And the estimate for seat belt use.

moutTCbelt <- Match(Y=stopsubt1c1$Belt, Tr=stopsubt1c1$treat, X=XTC, estimand="ATT", 
                    Weight.matrix=genoutTC)
summary(moutTCbelt)

Estimate...  -0.2767 
AI SE......  0.24119 
T-stat.....  -1.1472 
p.val......  0.25128 

Original number of observations..............  171 
Original number of treated obs...............  103 
Matched number of observations...............  103 
Matched number of observations  (unweighted).  108 

And the 95% confidence intervals.

belttcconup<-(moutTCbelt$est+moutTCbelt$se*1.96)
belttccondown<-(moutTCbelt$est-moutTCbelt$se*1.96)
belttcinter<-c(belttcconup,belttccondown)
belttcinter
[1]  0.1960264 -0.7494244

The data on passing.

moutTCpass <- Match(Y=stopsubt1c1$Pass, Tr=stopsubt1c1$treat, X=XTC, estimand="ATT", 
                    Weight.matrix=genoutTC)
summary(moutTCpass)

Estimate...  -2.0583 
AI SE......  1.0611 
T-stat.....  -1.9397 
p.val......  0.052419 

Original number of observations..............  171 
Original number of treated obs...............  103 
Matched number of observations...............  103 
Matched number of observations  (unweighted).  108 

And the 95% confidence intervals.

passtcconup<-(moutTCpass$est+moutTCpass$se*1.96)
passtccondown<-(moutTCpass$est-moutTCpass$se*1.96)
passtcinter<-c(passtcconup,passtccondown)
passtcinter
[1]  0.0215642 -4.1380691

The data on aggressive maneuvers.

moutTCagman <- Match(Y=stopsubt1c1$Agman, Tr=stopsubt1c1$treat, X=XTC, estimand="ATT", 
                     Weight.matrix=genoutTC)
summary(moutTCagman)

Estimate...  -1.8301 
AI SE......  0.62278 
T-stat.....  -2.9386 
p.val......  0.0032969 

Original number of observations..............  171 
Original number of treated obs...............  103 
Matched number of observations...............  103 
Matched number of observations  (unweighted).  108 

And the 95% confidence intervals.

agmantcconup<-(moutTCagman$est+moutTCagman$se*1.96)
agmantccondown<-(moutTCagman$est-moutTCagman$se*1.96)
agmantcinter<-c(agmantcconup,agmantccondown)
agmantcinter
[1] -0.6094532 -3.0507409

Aggressive behavior towards passengers.

moutTCagpassenger <- Match(Y=stopsubt1c1$Agpassenger, Tr=stopsubt1c1$treat, X=XTC, estimand="ATT", 
                           Weight.matrix=genoutTC)
summary(moutTCagpassenger)

Estimate...  0.019417 
AI SE......  0.076445 
T-stat.....  0.25401 
p.val......  0.79949 

Original number of observations..............  171 
Original number of treated obs...............  103 
Matched number of observations...............  103 
Matched number of observations  (unweighted).  108 

And the 95% confidence intervals.

agpassengertcconup<-(moutTCagpassenger$est+moutTCagpassenger$se*1.96)
agpassengertccondown<-(moutTCagpassenger$est-moutTCagpassenger$se*1.96)
agpassengertcinter<-c(agpassengertcconup,agpassengertccondown)
agpassengertcinter
[1]  0.1692489 -0.1304139

Estimates on aggressive behavior towards non-passengers.

moutTCagother <- Match(Y=stopsubt1c1$Agother, Tr=stopsubt1c1$treat, X=XTC, estimand="ATT", 
                       Weight.matrix=genoutTC)
summary(moutTCagother)

Estimate...  0.024272 
AI SE......  0.042318 
T-stat.....  0.57356 
p.val......  0.56626 

Original number of observations..............  171 
Original number of treated obs...............  103 
Matched number of observations...............  103 
Matched number of observations  (unweighted).  108 

And the 95% confidence intervals.

agothertcconup<-(moutTCagother$est+moutTCagother$se*1.96)
agothertccondown<-(moutTCagother$est-moutTCagother$se*1.96)
agothertcinter<-c(agothertcconup,agothertccondown)
agothertcinter
[1]  0.10721426 -0.05867057

Below we test for a contamination effect, comparing the first round control group to the second round control group. Overall, we see no contamination effect.

moutCC <- Match(Y=stopsubc1c2$total, Tr=stopsubc1c2$treat, X=XCC, estimand="ATT", 
                Weight.matrix=genoutCC)
summary(moutCC)

Estimate...  -0.070093 
AI SE......  2.2326 
T-stat.....  -0.031396 
p.val......  0.97495 

Original number of observations..............  175 
Original number of treated obs...............  107 
Matched number of observations...............  107 
Matched number of observations  (unweighted).  122 

And the 95% confidence intervals.

totccconup<-(moutCC$est+moutCC$se*1.96)
totcccondown<-(moutCC$est-moutCC$se*1.96)
totccinter<-c(totccconup,totcccondown)
totccinter
[1]  4.305749 -4.445935

The estimates for speed are significant.

moutCCspeed <- Match(Y=stopsubc1c2$speed, Tr=stopsubc1c2$treat, X=XCC, estimand="ATT", 
                         Weight.matrix=genoutCC)
summary(moutCCspeed)

Estimate...  -5.7664 
AI SE......  2.7952 
T-stat.....  -2.063 
p.val......  0.039114 

Original number of observations..............  175 
Original number of treated obs...............  107 
Matched number of observations...............  107 
Matched number of observations  (unweighted).  122 

And the 95% confidence intervals.

speedccconup<-(moutCCspeed$est+moutCCspeed$se*1.96)
speedcccondown<-(moutCCspeed$est-moutCCspeed$se*1.96)
speedccinter<-c(speedccconup,speedcccondown)
speedccinter
[1]  -0.287877 -11.244985

For telephone calls.

moutCCtelephone <- Match(Y=stopsubc1c2$Telephone, Tr=stopsubc1c2$treat, X=XCC, estimand="ATT", 
                         Weight.matrix=genoutCC)
summary(moutCCtelephone)

Estimate...  0.35047 
AI SE......  0.57437 
T-stat.....  0.61017 
p.val......  0.54175 

Original number of observations..............  175 
Original number of treated obs...............  107 
Matched number of observations...............  107 
Matched number of observations  (unweighted).  122 

And the 95% confidence intervals.

telephoneccconup<-(moutCCtelephone$est+moutCCtelephone$se*1.96)
telephonecccondown<-(moutCCtelephone$est-moutCCtelephone$se*1.96)
telephoneccinter<-c(telephoneccconup,telephonecccondown)
telephoneccinter
[1]  1.4762402 -0.7753056

Estimates on text messaging.

moutCCtexting <- Match(Y=stopsubc1c2$Texting, Tr=stopsubc1c2$treat, X=XCC, estimand="ATT", 
                       Weight.matrix=genoutCC)
summary(moutCCtexting)

Estimate...  0.065421 
AI SE......  0.064765 
T-stat.....  1.0101 
p.val......  0.31244 

Original number of observations..............  175 
Original number of treated obs...............  107 
Matched number of observations...............  107 
Matched number of observations  (unweighted).  122 

And the 95% confidence intervals.

textingccconup<-(moutCCtexting$est+moutCCtexting$se*1.96)
textingcccondown<-(moutCCtexting$est-moutCCtexting$se*1.96)
textingccinter<-c(textingccconup,textingcccondown)
textingccinter
[1]  0.19236047 -0.06151935

Estimates on smoking.

moutCCsmoking <- Match(Y=stopsubc1c2$Smoking, Tr=stopsubc1c2$treat, X=XCC, estimand="ATT", 
                       Weight.matrix=genoutCC)
summary(moutCCsmoking)

Estimate...  -0.088785 
AI SE......  0.48421 
T-stat.....  -0.18336 
p.val......  0.85451 

Original number of observations..............  175 
Original number of treated obs...............  107 
Matched number of observations...............  107 
Matched number of observations  (unweighted).  122 

And the 95% confidence intervals.

smokingccconup<-(moutCCsmoking$est+moutCCsmoking$se*1.96)
smokingcccondown<-(moutCCsmoking$est-moutCCsmoking$se*1.96)
smokingccinter<-c(smokingccconup,smokingcccondown)
smokingccinter
[1]  0.8602614 -1.0378315

Estimates for seatbelt use.

moutCCbelt <- Match(Y=stopsubc1c2$Belt, Tr=stopsubc1c2$treat, X=XCC, estimand="ATT", 
                    Weight.matrix=genoutCC)
summary(moutCCbelt)

Estimate...  0.35514 
AI SE......  0.27457 
T-stat.....  1.2934 
p.val......  0.19586 

Original number of observations..............  175 
Original number of treated obs...............  107 
Matched number of observations...............  107 
Matched number of observations  (unweighted).  122 

And the 95% confidence intervals.

beltccconup<-(moutCCbelt$est+moutCCbelt$se*1.96)
beltcccondown<-(moutCCbelt$est-moutCCbelt$se*1.96)
beltccinter<-c(beltccconup,beltcccondown)
beltccinter
[1]  0.8933018 -0.1830214

Estimates for passing.

moutCCpass <- Match(Y=stopsubc1c2$Pass, Tr=stopsubc1c2$treat, X=XCC, estimand="ATT", 
                    Weight.matrix=genoutCC)
summary(moutCCpass)

Estimate...  0.6729 
AI SE......  1.2614 
T-stat.....  0.53344 
p.val......  0.59373 

Original number of observations..............  175 
Original number of treated obs...............  107 
Matched number of observations...............  107 
Matched number of observations  (unweighted).  122 

And the 95% confidence intervals.

passccconup<-(moutCCpass$est+moutCCpass$se*1.96)
passcccondown<-(moutCCpass$est-moutCCpass$se*1.96)
passccinter<-c(passccconup,passcccondown)
passccinter
[1]  3.145309 -1.799514

Estimates for aggressive maneuvers.

moutCCagman <- Match(Y=stopsubc1c2$Agman, Tr=stopsubc1c2$treat, X=XCC, estimand="ATT", 
                     Weight.matrix=genoutCC)
summary(moutCCagman)

Estimate...  -1.4299 
AI SE......  0.744 
T-stat.....  -1.9219 
p.val......  0.054616 

Original number of observations..............  175 
Original number of treated obs...............  107 
Matched number of observations...............  107 
Matched number of observations  (unweighted).  122 

And the 95% confidence intervals.

agmanccconup<-(moutCCagman$est+moutCCagman$se*1.96)
agmancccondown<-(moutCCagman$est-moutCCagman$se*1.96)
agmanccinter<-c(agmanccconup,agmancccondown)
agmanccinter
[1]  0.02833208 -2.88814516

Estimates for aggressive behavior towards passengers.

moutCCagpassenger <- Match(Y=stopsubc1c2$Agpassenger, Tr=stopsubc1c2$treat, X=XCC, estimand="ATT", 
                           Weight.matrix=genoutCC)
summary(moutCCagpassenger)

Estimate...  0.03271 
AI SE......  0.029152 
T-stat.....  1.122 
p.val......  0.26184 

Original number of observations..............  175 
Original number of treated obs...............  107 
Matched number of observations...............  107 
Matched number of observations  (unweighted).  122 

And the 95% confidence intervals.

agpassengerccconup<-(moutCCagpassenger$est+moutCCagpassenger$se*1.96)
agpassengercccondown<-(moutCCagpassenger$est-moutCCagpassenger$se*1.96)
agpassengerccinter<-c(agpassengerccconup,agpassengercccondown)
agpassengerccinter
[1]  0.08984911 -0.02442855

Estimates for aggressive maneuvers.

moutCCagother <- Match(Y=stopsubc1c2$Agother, Tr=stopsubc1c2$treat, X=XCC, estimand="ATT", 
                       Weight.matrix=genoutCC)
summary(moutCCagother)

Estimate...  -0.028037 
AI SE......  0.050668 
T-stat.....  -0.55335 
p.val......  0.58002 

Original number of observations..............  175 
Original number of treated obs...............  107 
Matched number of observations...............  107 
Matched number of observations  (unweighted).  122 

And the 95% confidence intervals.

agotherccconup<-(moutCCagother$est+moutCCagother$se*1.96)
agothercccondown<-(moutCCagother$est-moutCCagother$se*1.96)
agotherccinter<-c(agotherccconup,agothercccondown)
agotherccinter

Below we present estimates for lasting effects. If there is no significant change, this potentially suggests a lack of lasting effect. The logic of this is that if there is no significant increase from the significantly lower level of dangerous driving behavior, then this suggests that dangerous driving behaviors remained at lower levels. Overall, we find a lasting effect, but dangerous driving behaviors increased.

moutTT <- Match(Y=stopsubt1t2$total, Tr=stopsubt1t2$treat, X=XTT, estimand="ATT", 
                Weight.matrix=genoutTT)
summary(moutTT)

Estimate...  2.6017 
AI SE......  2.2239 
T-stat.....  1.1699 
p.val......  0.24204 

Original number of observations..............  163 
Original number of treated obs...............  60 
Matched number of observations...............  60 
Matched number of observations  (unweighted).  81 

And the 95% confidence intervals.

totttconup<-(moutTT$est+moutTT$se*1.96)
totttcondown<-(moutTT$est-moutTT$se*1.96)
totttinter<-c(totttconup,totttcondown)
totttinter
[1]  6.960416 -1.757083

Estimates on speed.

moutTTspeed <- Match(Y=stopsubt1t2$speed, Tr=stopsubt1t2$treat, X=XTT, estimand="ATT", 
                     Weight.matrix=genoutTT)
summary(moutTTspeed)

Estimate...  0.22405 
AI SE......  3.4971 
T-stat.....  0.064067 
p.val......  0.94892 

Original number of observations..............  163 
Original number of treated obs...............  60 
Matched number of observations...............  60 
Matched number of observations  (unweighted).  81 

And the 95% confidence intervals.

speedttconup<-(moutTTspeed$est+moutTTspeed$se*1.96)
speedttcondown<-(moutTTspeed$est-moutTTspeed$se*1.96)
speedttinter<-c(speedttconup,speedttcondown)
speedttinter
[1]  7.078427 -6.630323

Estimates for telephone calls.

moutTTtelephone <- Match(Y=stopsubt1t2$Telephone, Tr=stopsubt1t2$treat, X=XTT, estimand="ATT", 
                         Weight.matrix=genoutTT)
summary(moutTTtelephone)

Estimate...  0.50833 
AI SE......  0.51126 
T-stat.....  0.99428 
p.val......  0.32009 

Original number of observations..............  163 
Original number of treated obs...............  60 
Matched number of observations...............  60 
Matched number of observations  (unweighted).  81 

And the 95% confidence intervals.

telephonettconup<-(moutTTtelephone$est+moutTTtelephone$se*1.96)
telephonettcondown<-(moutTTtelephone$est-moutTTtelephone$se*1.96)
telephonettinter<-c(telephonettconup,telephonettcondown)
telephonettinter
[1]  1.5103999 -0.4937333

Estimates for text messaging.

moutTTtexting <- Match(Y=stopsubt1t2$Texting, Tr=stopsubt1t2$treat, X=XTT, estimand="ATT", 
                       Weight.matrix=genoutTT)
summary(moutTTtexting)

Estimate...  0 
AI SE......  0.041833 
T-stat.....  0 
p.val......  1 

Original number of observations..............  163 
Original number of treated obs...............  60 
Matched number of observations...............  60 
Matched number of observations  (unweighted).  81 

And the 95% confidence intervals.

textingttconup<-(moutTTtexting$est+moutTTtexting$se*1.96)
textingttcondown<-(moutTTtexting$est-moutTTtexting$se*1.96)
textingttinter<-c(textingttconup,textingttcondown)
textingttinter
[1]  0.08199268 -0.08199268

Estimates for smoking.

moutTTsmoking <- Match(Y=stopsubt1t2$Smoking, Tr=stopsubt1t2$treat, X=XTT, estimand="ATT", 
                       Weight.matrix=genoutTT)
summary(moutTTsmoking)

Estimate...  0.475 
AI SE......  0.33738 
T-stat.....  1.4079 
p.val......  0.15916 

Original number of observations..............  163 
Original number of treated obs...............  60 
Matched number of observations...............  60 
Matched number of observations  (unweighted).  81 

And the 95% confidence intervals.

smokingttconup<-(moutTTsmoking$est+moutTTsmoking$se*1.96)
smokingttcondown<-(moutTTsmoking$est-moutTTsmoking$se*1.96)
smokingttinter<-c(smokingttconup,smokingttcondown)
smokingttinter
[1]  1.1362608 -0.1862608

Estimates for seat belt use.

moutTTbelt <- Match(Y=stopsubt1t2$Belt, Tr=stopsubt1t2$treat, X=XTT, estimand="ATT", 
                    Weight.matrix=genoutTT)
summary(moutTTbelt)

Estimate...  0.355 
AI SE......  0.33149 
T-stat.....  1.0709 
p.val......  0.2842 

Original number of observations..............  163 
Original number of treated obs...............  60 
Matched number of observations...............  60 
Matched number of observations  (unweighted).  81 

And the 95% confidence intervals.

beltttconup<-(moutTTbelt$est+moutTTbelt$se*1.96)
beltttcondown<-(moutTTbelt$est-moutTTbelt$se*1.96)
beltttinter<-c(beltttconup,beltttcondown)
beltttinter
[1]  1.0047162 -0.2947162

Estimates for illegal passing.

moutTTpass <- Match(Y=stopsubt1t2$Pass, Tr=stopsubt1t2$treat, X=XTT, estimand="ATT", 
                    Weight.matrix=genoutTT)
summary(moutTTpass)

Estimate...  0.031667 
AI SE......  1.6852 
T-stat.....  0.018791 
p.val......  0.98501 

Original number of observations..............  163 
Original number of treated obs...............  60 
Matched number of observations...............  60 
Matched number of observations  (unweighted).  81 

And the 95% confidence intervals.

passttconup<-(moutTTpass$est+moutTTpass$se*1.96)
passttcondown<-(moutTTpass$est-moutTTpass$se*1.96)
passttinter<-c(passttconup,passttcondown)
passttinter
[1]  3.334643 -3.271309

Estimates for aggressive maneuvers.

moutTTagman <- Match(Y=stopsubt1t2$Agman, Tr=stopsubt1t2$treat, X=XTT, estimand="ATT", 
                     Weight.matrix=genoutTT)
summary(moutTTagman)

Estimate...  1.215 
AI SE......  0.76514 
T-stat.....  1.5879 
p.val......  0.1123 

Original number of observations..............  163 
Original number of treated obs...............  60 
Matched number of observations...............  60 
Matched number of observations  (unweighted).  81 

And the 95% confidence intervals.

agmanttconup<-(moutTTagman$est+moutTTagman$se*1.96)
agmanttcondown<-(moutTTagman$est-moutTTagman$se*1.96)
agmanttinter<-c(agmanttconup,agmanttcondown)
agmanttinter
[1]  2.7146737 -0.2846737

Estimates for aggression towards passengers.

moutTTagpassenger <- Match(Y=stopsubt1t2$Agpassenger, Tr=stopsubt1t2$treat, X=XTT, estimand="ATT", 
                           Weight.matrix=genoutTT)
summary(moutTTagpassenger)

Estimate...  0.033333 
AI SE......  0.041483 
T-stat.....  0.80354 
p.val......  0.42166 

Original number of observations..............  163 
Original number of treated obs...............  60 
Matched number of observations...............  60 
Matched number of observations  (unweighted).  81 

And the 95% confidence intervals.

agpassengerttconup<-(moutTTagpassenger$est+moutTTagpassenger$se*1.96)
agpassengerttcondown<-(moutTTagpassenger$est-moutTTagpassenger$se*1.96)
agpassengerttinter<-c(agpassengerttconup,agpassengerttcondown)
agpassengerttinter
[1]  0.11463987 -0.04797321

Estimates for aggressive towards others.

moutTTagother <- Match(Y=stopsubt1t2$Agother, Tr=stopsubt1t2$treat, X=XTT, estimand="ATT", 
                       Weight.matrix=genoutTT)
summary(moutTTagother)