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 secondsgenoutCC <- 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 secondsgenoutTT <- 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 secondsMatch 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 -10.581186Estimates 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 -8.412971Estimate 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 -2.517376Estimates 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 -0.04328763Estimates 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 -2.4372246And 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 -0.7494244The 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 -4.1380691The 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 -3.0507409Aggressive 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 -0.1304139Estimates 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 -0.05867057Below 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 -4.445935The 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 -11.244985For 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 -0.7753056Estimates 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 -0.06151935Estimates 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 -1.0378315Estimates 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 -0.1830214Estimates 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 -1.799514Estimates 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 -2.88814516Estimates 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 -0.02442855Estimates 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)
agotherccinterBelow 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 -1.757083Estimates 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 -6.630323Estimates 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 -0.4937333Estimates 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 -0.08199268Estimates 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 -0.1862608Estimates 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 -0.2947162Estimates 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 -3.271309Estimates 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 -0.2846737Estimates 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 -0.04797321Estimates for aggressive towards others.
moutTTagother <- Match(Y=stopsubt1t2$Agother, Tr=stopsubt1t2$treat, X=XTT, estimand="ATT",
Weight.matrix=genoutTT)
summary(moutTTagother)
Estimate... -0.016667
AI SE...... 0.017068
T-stat..... -0.97648
p.val...... 0.32883
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.
agotherttconup<-(moutTTagother$est+moutTTagother$se*1.96)
agotherttcondown<-(moutTTagother$est-moutTTagother$se*1.96)
agotherttinter<-c(agotherttconup,agotherttcondown)
agotherttinter
[1] 0.01678678 -0.05012011