########################################################################

# Sample Selection model (Type II Tobit model).

# Probit equation

# dependent variable: 1 if work >0 and 0 if work=0

# independent variables: child, age, edu, hinc

# Regression equation

# dependent variable: wage

# independent variables: age, edu, year

#

########################################################################

model

{

# For those whose wages are observed, generate a latent variable

# for the probit equation and consider the bivariate normal density.

for(i in 1:428)

{

ys[i] ~ dnorm(mus[i], 1)I(0,)

mus[i] <- b[1]+b[2]*(child[i]-mean(child[]))/sd(child[])

+b[3]*(age[i]-mean(age[]))/sd(age[])

+b[4]*(edu[i]-mean(edu[]))/sd(edu[])

+b[5]*(hinc[i]-mean(hinc[]))/sd(hinc[])

z[i] <- 0

z[i] ~ dpois(psi[i])

l.lik[i] <- -0.5*log(sigma2*(1-rho*rho))

-0.5*pow(wage[i]-mu[i]-rho*sigma*(ys[i]-mus[i]), 2)

/(sigma2*(1-rho*rho))

mu[i] <- a[1]+a[2]*(age[i] -mean(age[]))/sd(age[])

+a[3]*(edu[i] -mean(edu[]))/sd(edu[])

+a[4]*(year[i]-mean(year[]))/sd(year[])

psi[i] <- -l.lik[i] + 10000

}

# For those whose wages are not observed, we only consider

# the probit equation:

for(i in 429:n)

{

ones[i] <- 1-work[i] # this is 1 since work[i]=0 for i=429,...,n

ones[i] ~ dbern(p[i])

p[i] <- phi( -mus[i] )

mus[i] <- b[1]+b[2]*(child[i]-mean(child[]))/sd(child[])

+b[3]*(age[i]-mean(age[]))/sd(age[])

+b[4]*(edu[i]-mean(edu[]))/sd(edu[])

+b[5]*(hinc[i]-mean(hinc[]))/sd(hinc[])

}

b[1] ~ dnorm( 0, 1.0E-6)

b[2] ~ dnorm( 0, 1.0E-6)

b[3] ~ dnorm( 0, 1.0E-6)

b[4] ~ dnorm( 0, 1.0E-6)

b[5] ~ dnorm( 0, 1.0E-6)

a[1] ~ dnorm( 0, 1.0E-6)

a[2] ~ dnorm( 0, 1.0E-6)

a[3] ~ dnorm( 0, 1.0E-6)

a[4] ~ dnorm( 0, 1.0E-6)

rho ~ dunif( -1, 1)

lsigma ~ dnorm( 0, 1.0E-6)

sigma <- exp( lsigma )

sigma2 <- exp( lsigma*2 )

b.cons <- b[1]-b[2]*mean(child[])/sd(child[])-b[3]*mean(age[])/sd(age[])

-b[4]*mean(edu[])/sd(edu[])-b[5]*mean(hinc[])/sd(hinc[])

b.child <- b[2]/sd(child[])

b.age <- b[3]/sd(age[])

b.edu <- b[4]/sd(edu[])

b.hinc <- b[5]/sd(hinc[])

a.cons <- a[1]-a[2]*mean(age[])/sd(age[])

-a[3]*mean(edu[])/sd(edu[])-a[4]*mean(year[])/sd(year[])

a.age <- a[2]/sd(age[])

a.edu <- a[3]/sd(edu[])

a.year <- a[4]/sd(year[])

}

# Initial values (set the values below and click on "load inits".

# Further click on "gen inits" for ys.

list( a = c(0, 0, 0, 0), b = c(0, 0, 0, 0, 0), rho=0, lsigma = 0)

# Data 1 uncensored 428. censored 325.

list(n = 753)

# Data 2

work[] child[] age[] edu[] wage[] hinc[] year[]

1 1 32 12 3.354 4.0288 14

1 0 30 12 1.3889 8.4416 5

1 1 35 12 4.5455 3.5807 15

1 0 34 12 1.0965 3.5417 6

1 1 31 14 4.5918 10 7

1 0 54 12 4.7421 6.7106 33

1 0 37 16 8.3333 3.4277 11

1 0 54 12 7.8431 2.5485 35

1 0 48 12 2.1262 4.2206 24

1 0 39 12 4.6875 5.7143 21

1 0 33 12 4.063 9.7959 15

1 0 42 11 4.5918 8 14

1 1 30 12 2.0833 5.3004 0

1 0 43 12 2.2668 4.3413 14

1 0 43 10 3.6797 10.87 6

1 0 35 11 1.3472 9.1499 9

1 0 43 12 3.2143 6.1224 20

1 0 39 12 5.175 6.1498 6

1 0 45 12 2 6.917 23

1 0 35 12 7.5529 4.7103 9

1 0 42 16 3.5052 3.131 5

1 0 30 12 3.5714 4 11

1 0 48 13 3.25 7.2227 18

1 0 45 12 3.25 7.9652 15

1 1 31 12 2.1545 4.0884 4

1 0 43 17 3.7879 14.181 21

1 0 59 12 4 6.5359 31

1 0 32 12 4.7269 8.5 9

1 1 31 17 7.2559 6.2762 7

1 0 42 12 5.8671 5.2083 7

1 0 50 11 1.5385 2.7821 32

1 0 59 16 2.459 4.9107 11

1 0 36 13 5.8511 5.8669 16

1 0 51 12 3.5714 7.52 14

1 0 45 16 3.8068 7.5449 27

1 0 42 11 2.4638 5.538 0

1 0 46 12 2.3753 6.9231 17

1 0 46 10 4.5351 5 28

1 0 51 14 5.6183 7.3064 24

1 0 30 17 14.631 11.218 11

1 1 30 12 2.6786 3.8462 1

1 0 57 12 3.9194 5.8366 14

1 1 31 16 2.5729 13.725 6

1 0 48 12 4.5375 6.3493 10

1 0 30 12 2 5.2528 6

1 0 34 12 3.4722 1.3075 4

1 0 48 16 2.0161 2.7998 10

1 0 45 12 4.5716 2.6961 22

1 0 51 12 2.2727 7.5688 16

1 0 30 12 2.6375 3.4077 6

1 0 46 12 2.2899 6.5401 12

1 0 58 12 1.0989 7.2139 32

1 0 37 12 1.1765 6.2732 15

1 0 52 8 1.6 5.8824 17

1 0 52 10 1.8762 3.8095 34

1 0 31 16 4.0437 6.3776 9

1 0 55 14 9.6354 6.0453 37

1 0 34 17 8.0409 8.8119 10

1 0 55 14 4.599 8.8765 35

1 0 39 12 2.1429 3.342 6

1 0 40 14 4.4 3.1836 19

1 0 43 12 3.5354 6.9149 10

1 0 48 8 2.7174 5.5147 11

1 0 47 12 6.25 5.2808 15

1 0 41 12 11.933 3.2 12

1 0 36 8 3.5931 5.8791 12

1 0 46 17 6.9444 6.25 14

1 0 34 12 2.9167 7.4879 11

1 0 41 12 3.0769 6.9767 9

1 0 51 12 3.75 8 24

1 0 33 12 5.7259 4.1319 12

1 0 52 12 3.6757 14.476 13

1 0 58 9 5.1648 5.7343 29

1 2 34 10 8.2237 3.3742 11

1 0 31 12 4.3365 1.8333 13

1 0 48 12 4.9819 5.6025 19

1 0 32 12 0.3571 4.2975 2

1 0 49 17 2.9674 3.9161 24

1 2 32 15 1 4.8787 9

1 0 58 12 2.5554 9.2402 6

1 0 50 6 0.8602 6.6519 22

1 0 60 14 1 4.6217 30

1 0 50 12 2.9261 9.9741 10

1 0 56 14 3.5461 20.918 6

1 0 51 9 1.6264 1.94 29

1 0 54 17 8.3333 8.8351 29

1 0 59 13 3.0952 4.6667 36

1 0 46 9 2.7 5.1894 19

1 0 46 15 5.2521 18.724 8

1 1 39 12 1.4154 10.417 13

1 0 44 12 4.7986 8.8458 16

1 2 33 12 1.6667 7.569 11

1 1 33 12 1.1217 4.4507 15

1 0 48 12 0.5 9.3204 6

1 0 31 12 0.7143 9.1687 13

1 0 45 12 2.7961 6.4834 22

1 0 45 12 4.8583 7.8125 24

1 0 32 13 1.7435 11.404 2

1 0 47 12 2.4631 5.087 6

1 0 34 13 2.4213 6.9758 2

1 0 37 12 1.5345 5.2863 2

1 0 36 12 2.8818 11.562 14

1 1 47 12 2.4069 8.6061 9

1 0 48 16 5.2326 12.805 11

1 0 42 12 3.75 6.6946 9

1 0 33 13 1.3889 8.3333 6

1 0 46 11 4 4.1667 19

1 0 47 12 3.2313 5.4759 26

1 0 44 12 3.4014 5.144 19

1 0 36 12 1.3333 11.667 3

1 2 31 17 9.3023 7.2917 7

1 0 55 14 4.5 4.8611 28

1 0 45 16 4.6235 12.164 13

1 0 47 17 3.9557 10.823 9

1 0 46 12 5.814 12.478 15

1 0 49 11 0.5 6.162 20

1 0 49 12 4.0816 7.1846 29

1 0 45 12 6 7.093 9

1 1 38 17 3.6667 17.826 1

1 0 47 10 3.8613 6.393 8

1 0 54 13 2.7629 11.719 19

1 0 41 11 2.931 4.7883 23

1 0 43 12 4.3884 4.2328 3

1 1 31 16 5.4167 7.1066 13

1 0 47 17 9.8611 8.3333 8

1 0 35 12 0.1616 6.9888 17

1 0 45 16 0.3826 9.5913 4

1 1 33 12 3.6364 6.0204 15

1 0 54 16 2.3747 2.9686 11

1 0 35 8 4.6667 7 7

1 1 31 12 1.8519 7.1627 0

1 0 55 12 5.2 4.7364 0

1 0 34 12 3.2986 8.781 10

1 0 38 13 8.5333 9.8315 8

1 0 45 11 2.0635 6.6496 2

1 0 47 12 2.5641 9.8485 4

1 0 39 12 2.1875 9.8039 6

1 1 36 14 6.25 8.2604 18

1 1 33 12 3.3333 9.0676 3

1 0 50 12 4.4444 8.5082 22

1 0 58 12 6.6335 7.1565 33

1 0 49 17 8.4224 7.0423 28

1 0 41 14 4.3956 6.5934 23

1 0 51 12 2.4457 4.3457 27

1 0 53 9 1.2245 7.2362 11

1 1 36 12 1.625 1.74 6

1 0 46 12 3 6.8966 11

1 0 36 12 4.7269 5.0455 14

1 0 53 14 1.1293 17.25 17

1 0 40 16 7.4024 8.3333 17

1 0 42 17 4.4595 9.375 14

1 1 33 15 2.4725 8.279 11

1 0 43 12 1.8824 4.5 7

1 1 31 16 4 4.1995 8

1 0 47 17 8.19 16.064 6

1 0 54 17 7.0968 11.277 8

1 1 33 12 1.6667 0.5494 4

1 0 43 16 3.445 9.548 25

1 0 46 13 4.2347 5.3333 24

1 0 35 12 2.7778 5.4545 11

1 0 37 11 1.8892 5.9026 19

1 0 37 16 5.0352 9.8576 9

1 0 34 14 1.25 11.685 19

1 1 43 16 2.8571 6.2278 14

1 0 46 12 4.1167 9.1748 22

1 0 35 9 1.7778 6.2972 6

1 0 46 17 13.554 10.471 23

1 0 46 14 4.5627 14.583 15

1 0 43 12 2.1277 6.2143 6

1 0 30 12 2.9891 6.6288 11

1 0 41 11 2.5654 3.8246 2

1 0 54 12 5.6122 7.8125 22

1 0 31 12 2.8054 3.4806 10

1 0 44 10 1.607 5.55 14

1 0 32 12 2.25 11.594 12

1 0 47 5 2.0325 9.5238 9

1 0 46 17 5.532 7.2805 13

1 0 37 11 1.5845 3 18

1 0 51 12 3.7879 10.4 8

1 0 49 12 3 10.05 11

1 0 36 14 8.6538 8.7866 9

1 0 39 11 4.2105 10.14 9

1 0 48 12 4.6875 5 14

1 0 38 14 4.0984 11.888 9

1 0 40 12 25 13.333 2

1 1 39 10 2.6331 8.6806 12

1 0 37 16 6 7.2917 15

1 0 49 13 5.4126 3.5242 11

1 0 33 12 0.6642 6.1818 7

1 0 30 12 1.25 2.9755 9

1 0 54 12 2.2754 4.5769 19

1 0 39 11 3.4615 3.1056 11

1 0 43 12 4.1667 5.8228 8

1 0 31 9 4.4687 7.5 13

1 0 33 13 1.75 8.3333 4

1 0 40 12 3.6694 9.2347 7

1 0 36 12 6.5826 5.5 19

1 0 51 12 2.6 6 14

1 0 44 13 4.8651 6.6667 14

1 0 42 16 5.787 13.061 3

1 0 40 12 4.5408 12.794 9

1 1 34 16 9.5057 12 7

1 0 30 17 10.638 6.73 7

1 0 54 12 1.1111 6.679 14

1 0 51 12 4.0541 4.3945 29

1 0 44 9 2.6871 4.6875 19

1 0 43 12 2.9762 7.6989 14

1 0 34 12 3.1728 4.3287 16

1 0 45 13 3.55 6.7416 10

1 0 39 12 17.907 9.3525 12

1 0 50 12 3.4174 9.1429 24

1 0 52 12 3.3333 2.6418 6

1 0 41 12 3.886 7.365 9

1 0 59 10 2.3111 1.4066 14

1 0 52 12 1.7108 1.5192 26

1 0 46 16 2.1143 3.3439 7

1 1 41 12 9.9331 2.9106 4

1 0 33 11 3.0277 4.3834 15

1 0 45 12 1.8605 4.2098 23

1 1 36 10 0.1282 3.7129 1

1 0 48 12 6.6327 10.606 29

1 0 47 12 5.6391 8.6382 9

1 0 45 12 1.599 3.8367 6

1 0 37 12 2.6667 7.1671 11

1 0 46 16 7.9208 8.8634 17

1 0 43 17 5.3398 7.7369 6

1 0 42 12 4 9.1702 7

1 1 34 17 6.0449 11.29 2

1 0 52 12 6.25 6.3269 24

1 0 37 12 2.9762 4.7468 4

1 0 37 12 4.2339 4.8885 11

1 0 52 8 3.4916 2.4842 25

1 1 30 12 4.9645 8.547 11

1 0 31 13 2.7692 6.6379 2

1 0 38 12 3.6585 3.1863 19

1 0 43 12 5.3935 4.5466 7

1 0 49 8 0.6564 12.789 2

1 0 55 12 4.7688 6.528 20

1 0 38 17 8.5551 16.403 10

1 0 52 17 10.459 7 19

1 0 48 12 2.6375 13.542 17

1 0 32 13 6.8493 6.713 12

1 0 32 12 5.0872 5.4487 11

1 0 38 12 0.9615 6.6872 6

1 0 46 12 4.3066 9.0943 10

1 0 40 12 7.0667 6.5337 4

1 0 31 9 2.5253 4.686 2

1 0 43 10 7.8947 8 13

1 0 51 12 4.1489 4.053 21

1 1 30 16 8.1746 8.1633 9

1 0 52 13 9.5971 16.5 4

1 1 30 8 2.0164 3.4615 2

1 0 51 16 7.6218 11.361 19

1 0 31 13 3.1975 3.8911 4

1 0 34 12 1.6 9.6059 9

1 0 49 11 4.0984 2.6687 14

1 1 35 13 1.4815 8.8865 6

1 1 53 12 3.6364 6.3636 24

1 0 32 12 1 17.728 1

1 0 38 10 2.6042 5.2923 13

1 0 54 12 1.75 15.686 3

1 0 47 17 4.8 5.2423 10

1 0 45 15 5.53 3.0395 16

1 0 47 16 4.0984 16.745 9

1 0 59 10 1.25 3.75 19

1 0 32 11 1.6667 5.814 4

1 0 45 12 3.7901 10.833 10

1 0 40 12 2.3636 9.0029 5

1 0 47 14 10.204 6.54 7

1 1 36 16 6.8182 5.9783 3

1 0 56 14 7.2146 8.6248 38

1 0 41 8 2.4484 4.8329 16

1 0 48 7 1.1987 10.909 13

1 1 36 12 1.641 3.8864 1

1 0 41 12 1.7821 8.4574 7

1 0 41 14 2.9412 5 15

1 0 36 12 4.9689 7.4212 10

1 0 37 12 1.8605 4.4938 2

1 0 38 12 8.0357 7.0252 19

1 0 43 14 3.9716 3.2788 25

1 0 54 16 3.0416 1.2861 25

1 0 38 12 2.907 5.5556 7

1 1 30 12 3.0612 7.4951 15

1 0 49 12 4.8966 22.109 11

1 0 45 13 4.0161 9.058 25

1 0 51 13 5.5556 11.538 19

1 0 34 10 1.2227 6.0468 4

1 0 34 12 2.6839 1.3653 14

1 0 41 12 2.6738 16.55 19

1 0 49 12 9.3294 8.7719 18

1 0 32 12 3.1381 9.6354 14

1 0 32 14 0.5426 6.6788 11

1 0 32 17 8.6111 7.5727 4

1 0 47 10 3.6687 7.8125 29

1 0 39 9 2.3185 1.9835 21

1 0 49 12 2.882 9.5238 24

1 0 37 12 3.1666 6.425 19

1 0 59 16 3.6458 10.984 31

1 0 50 12 6.25 12.766 28

1 0 32 17 10.249 9.5545 15

1 0 46 12 3.2096 5.4967 27

1 0 43 17 7.6522 9.7618 13

1 0 37 11 1.9724 5 4

1 0 32 16 4.698 5.9995 10

1 0 39 11 2.1234 6.9149 8

1 0 34 13 2.3333 4.9669 4

1 0 39 11 2.3896 2.3896 18

1 0 45 8 1.2564 4.5122 3

1 0 50 11 1.0938 9.2308 11

1 0 40 12 3.75 11.458 8

1 0 30 10 3.3072 7.6993 10

1 0 57 17 5.1352 4.183 33

1 0 39 12 6.6327 8.3333 19

1 0 53 12 4.5645 5.1042 35

1 0 48 17 11.848 8.2271 21

1 0 46 14 3.75 5.6634 7

1 0 47 12 4.3651 5.222 18

1 0 43 12 3.9333 7.19 4

1 0 47 12 3.3333 13.907 12

1 0 47 12 3.3159 6.0163 16

1 0 47 12 3.5616 7.6782 14

1 0 46 12 1.6 5.3112 3

1 0 34 9 2.2255 4.1228 1

1 0 48 10 4.7872 7.8125 27

1 0 30 12 5.8065 4.6053 12

1 0 51 12 2.3585 3.9746 6

1 0 52 12 2 15.063 9

1 0 37 12 1.9 4.5064 2

1 0 32 12 5.123 5.8824 6

1 0 36 17 5.4945 10.228 9

1 0 35 12 6.3218 6.5789 16

1 0 45 17 7.1429 8.8145 22

1 0 56 12 2.375 2.9528 26

1 0 40 10 2.5429 4.5173 11

1 1 45 12 2.1798 5.1282 11

1 0 32 12 2.6 4.1451 15

1 0 45 12 3.7294 8.7912 13

1 0 40 12 4.375 7.3061 6

1 0 38 12 4.4433 6.0069 20

1 0 49 12 4.2877 8.3333 17

1 0 47 16 1.6667 11.529 8

1 0 52 13 3.2558 6.6667 13

1 0 34 13 5.4113 8.6806 15

1 0 44 12 2.205 3.468 14

1 0 36 16 4.0625 3.0321 14

1 0 50 17 0.6482 13.807 6

1 0 45 12 5.3826 6.2174 24

1 0 44 14 0.1709 4.2308 10

1 0 57 12 23.467 11.47 2

1 0 35 17 9.5785 12.245 9

1 0 46 12 3.6948 9.2105 23

1 2 30 14 2.2222 6.3309 12

1 0 42 12 1.749 7.8039 8

1 0 34 12 1.1594 7.6754 16

1 0 45 17 6.9901 15.582 10

1 1 35 16 3.9683 9.6436 7

1 0 40 16 21.429 12.886 19

1 0 32 12 0.4762 6.5511 2

1 0 54 9 2.1505 8.25 9

1 0 38 12 1.8578 1.3792 14

1 0 43 12 4.3295 5.2632 9

1 0 54 16 8.9286 10.526 16

1 0 39 14 2.7778 10 7

1 0 37 12 2.6565 12.548 6

1 0 46 12 2.5 4.6207 22

1 0 56 11 18.267 1.854 9

1 0 41 12 0.819 7.4266 9

1 0 45 16 2 13.298 14

1 0 44 17 15.385 26.578 17

1 0 50 17 6.4775 13.959 12

1 0 37 14 8.3333 13.126 13

1 0 44 12 4.5503 7.8883 8

1 0 32 14 2.4958 8.656 10

1 1 34 12 4.4797 3.4109 16

1 0 32 10 2.2324 4.6256 1

1 0 37 12 2.071 4.2105 6

1 0 44 13 1.676 4.2708 4

1 0 34 16 3.4091 3.3424 8

1 1 33 12 2.5 8.6359 4

1 0 43 7 3.9609 6.7581 15

1 0 35 16 6.2275 0.5843 7

1 0 43 14 3.9286 5.335 14

1 0 34 12 2.9 4.7101 16

1 0 36 10 4.0816 4.9797 15

1 0 41 12 2.85 2.9762 23

1 0 41 16 7.0153 12.744 19

1 0 35 10 2.9388 3.8462 4

1 1 32 12 1.9231 7.2233 12

1 0 30 14 6.875 8.8077 12

1 0 43 12 3.9002 5.3864 25

1 0 54 6 2 3.6846 14

1 0 35 15 4.9763 1.8437 14

1 0 50 12 1.2019 1.3013 11

1 1 34 17 22.5 7.2222 7

1 0 52 14 6.8677 7.3529 18

1 0 35 13 3.5613 11.864 4

1 0 55 6 1.9792 5.3327 37

1 0 35 16 5.3713 5.5 13

1 0 49 14 1.7442 7.2308 14

1 2 38 15 5.0948 7 17

1 0 42 14 2.5 7.2137 5

1 0 48 8 3.825 4.5 2

1 0 51 14 1 8.8632 0

1 0 43 12 3.0717 9.3333 3

1 0 43 12 1.7163 3.8263 21

1 0 38 12 4.0209 5.4448 20

1 0 44 12 5.4585 8.7634 19

1 1 36 12 25 8.4 4

1 0 38 12 2.3897 5.102 19

1 0 47 8 3.2134 7.4101 11

1 0 34 12 3.377 5.3527 14

1 1 40 17 1.7778 19.562 8

1 0 31 12 3.1633 5.0783 13

1 0 46 12 2.7027 0.5128 24

1 0 36 14 1.6927 5.402 1

1 1 39 13 0.2137 7.8646 1

1 0 36 17 6.7669 6.0531 3

1 0 37 8 1.7407 5 4

1 0 39 12 2.5 7.6829 21

1 1 36 11 4.4859 6.0386 10

1 0 49 12 2.572 5.851 13

1 1 45 12 3.46 6.7416 9

1 2 32 17 4.7826 8.1081 14

1 0 36 10 2.3118 5.3061 2

1 0 40 12 5.3061 7.2709 21

1 0 43 13 5.8675 8.1776 22

1 0 33 12 3.4091 7.1006 14

1 0 30 12 4.0816 6.5844 7

0 0 49 12 0 7.8529 2

0 2 30 16 0 11.929 5

0 1 30 12 0 18 12

0 0 41 12 0 10.022 1

0 0 45 12 0 9.3333 12

0 0 43 12 0 6.085 4

0 0 42 13 0 5.7054 9

0 0 60 12 0 9.1185 9

0 0 57 12 0 7.2072 6

0 0 38 10 0 5.3146 5

0 0 56 12 0 8.2828 5

0 0 32 12 0 7.0578 8

0 0 49 7 0 6.1659 2

0 0 55 12 0 2.7 6

0 1 36 9 0 7.6628 0

0 0 44 12 0 1.8491 3

0 0 44 10 0 3.8462 7

0 1 35 14 0 9.0543 3

0 2 44 14 0 9.375 10

0 0 45 12 0 8.3333 3

0 1 34 12 0 9.3733 2

0 2 30 17 0 4.4818 12

0 0 39 8 0 5.3476 15

0 0 36 12 0 3.2027 5

0 0 38 17 0 12.801 4

0 0 53 12 0 6.9103 10

0 0 36 12 0 4.2293 1

0 1 32 12 0 4.4479 8

0 0 51 9 0 8.0523 20

0 0 38 11 0 7.5057 4

0 2 33 12 0 7.4747 7

0 0 54 12 0 6.3218 10

0 0 38 9 0 2.1354 3

0 2 30 11 0 4.2092 5

0 2 34 12 0 0.7595 10

0 0 34 9 0 4.8077 0

0 0 50 12 0 12.788 3

0 2 30 17 0 7.1429 10

0 0 38 12 0 15.152 2

0 0 54 14 0 7.9082 10

0 1 30 12 0 6.9728 4

0 0 55 12 0 4.9181 0

0 0 51 10 0 8.3112 10

0 0 44 12 0 7.1429 5

0 0 53 12 0 5.6889 0

0 0 42 10 0 7.0833 0

0 0 38 12 0 5.6922 19

0 1 38 13 0 11.4 2

0 1 41 12 0 1.4152 12

0 0 35 8 0 2.7507 5

0 1 33 12 0 6.6182 5

0 0 48 13 0 9.2725 5

0 0 47 12 0 12.287 10

0 0 34 12 0 2.8549 0

0 2 33 13 0 7.9709 4

0 3 31 13 0 19.444 3

0 0 58 8 0 1.4806 2

0 0 49 12 0 1.8918 1

0 0 55 8 0 6.9215 0

0 0 44 14 0 10.692 1

0 0 44 9 0 9.9474 1

0 0 36 16 0 19 6

0 0 38 12 0 7.4882 12

0 0 37 16 0 9.259 6

0 0 47 12 0 11.322 9

0 0 47 12 0 6.447 14

0 1 32 12 0 6.5778 13

0 1 43 12 0 6.1 8

0 1 42 11 0 4.2308 0

0 0 56 12 0 2.217 1

0 0 38 13 0 6.5189 3

0 0 52 12 0 6.4923 13

0 0 50 12 0 11.364 3

0 0 33 16 0 9.8765 8

0 0 44 16 0 7.6576 8

0 0 41 12 0 8.3017 18

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END