##############################################################
## WinBUGS code for MN breast cancer late detection examples##
## Data in alphabetic order                                 ##
## Use "MVCmnPlot.txt" and "plotMVC.txt"to produce maps     ##
##		08/15/2005 Haijun Ma			    ##
##############################################################

#######################################################
## 3.1 Global Areal Smoothing and Boundary Detection ##
#######################################################

model{
for(i in 1: n.area){

Y[i] ~ dpois(mu[i])
log(mu[i]) <- log(E[i]) + gamma[1] + gamma[2] * X[i] + theta[i]
RR[i] <- mu[i] / E[i]
theta[i] ~ dnorm(0, tau)
}

# calculate "boundary likelihood values"

for(i in 1:n.edge){
delta[i] <- abs(RR[ind1[i]] - RR[ind2[i]] )
deltamy[i] <- RR[ind1[i]] - RR[ind2[i]] 
}

gamma[1] ~ dflat()
gamma[2] ~ dflat()
tau ~ dgamma(0.01, 0.01)
}


## Initials:
list(tau=10, 
gamma=c(0, 0), theta=c( 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 ))

list(tau=1, 
gamma=c(1, -1), theta=c( 10,10,10,10,10,10,10,10,10,10, 10,10,10,10,10,
10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,
10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,
10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10))


## Data: Y= Breast Cancer late detection, 
## X= centered pct respodente NOT having mamgram, mean(X)= 37.59067

list( n.area=87,  n.edge=211,
ind1=c(1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 
3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 
7, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 10, 10, 10, 10, 10, 11, 11, 
11, 11, 11, 11, 12, 12, 12, 12, 12, 13, 13, 13, 14, 14, 14, 15, 
15, 15, 15, 16, 17, 17, 17, 17, 18, 18, 19, 19, 19, 19, 19, 19, 
20, 20, 20, 20, 21, 21, 21, 21, 21, 22, 22, 22, 23, 23, 23, 23, 
24, 24, 24, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 28, 29, 30, 
30, 30, 31, 31, 32, 32, 33, 33, 34, 34, 34, 34, 34, 35, 35, 36, 
36, 37, 37, 38, 39, 40, 40, 40, 40, 40, 41, 41, 41, 42, 42, 42, 
43, 43, 44, 44, 44, 45, 46, 46, 46, 46, 47, 47, 47, 48, 48, 49, 
49, 50, 51, 51, 51, 52, 52, 53, 54, 55, 55, 56, 56, 56, 57, 57, 
59, 60, 61, 61, 61, 62, 64, 64, 65, 65, 66, 66, 66, 69, 70, 70, 
73, 73, 74, 75, 75, 77, 78, 79),

ind2=c(9, 11, 18, 31, 33, 48, 58, 72, 13, 27, 30, 62, 70, 82, 14, 
15, 29, 43, 54, 56, 80, 11, 15, 29, 31, 36, 39, 44, 57, 68, 48, 
49, 70, 73, 37, 75, 76, 78, 8, 22, 40, 45, 52, 81, 83, 17, 52, 
64, 65, 83, 58, 72, 27, 46, 69, 71, 86, 18, 29, 31, 49, 77, 80, 
34, 37, 65, 76, 87, 30, 58, 82, 54, 56, 84, 29, 43, 57, 60, 38, 
32, 51, 64, 83, 48, 49, 25, 27, 62, 66, 69, 82, 25, 50, 55, 74, 
26, 56, 61, 73, 77, 24, 45, 81, 28, 50, 55, 85, 50, 74, 81, 55, 
66, 79, 56, 75, 78, 84, 62, 69, 86, 85, 80, 33, 48, 70, 36, 72, 
45, 53, 48, 58, 47, 61, 65, 73, 76, 44, 68, 39, 72, 76, 87, 72, 
68, 52, 66, 69, 71, 81, 42, 59, 87, 51, 64, 87, 54, 60, 57, 60, 
68, 83, 47, 65, 71, 86, 65, 73, 86, 49, 70, 73, 77, 55, 53, 59, 
64, 65, 71, 67, 60, 79, 85, 77, 80, 84, 60, 63, 67, 63, 73, 75, 
76, 82, 65, 87, 71, 87, 69, 74, 81, 71, 73, 86, 77, 86, 81, 76, 
78, 80, 84, 85),

Y = c(14, 121, 25, 18, 17, 2, 32, 20, 15, 28, 27, 8, 26, 27, 8, 2, 
10, 38, 170, 12, 19, 12, 11, 20, 36, 5, 722, 10, 11, 23, 24, 
10, 3, 30, 3, 10, 8, 13, 3, 16, 2, 10, 6, 11, 20, 29, 20, 20, 
18, 23, 5, 15, 12, 6, 76, 33, 14, 15, 7, 30, 7, 328, 2, 11, 12, 
42, 8, 13, 19, 27, 18, 143, 65, 31, 3, 13, 11, 5, 10, 14, 16, 
100, 8, 8, 38, 35, 8),

E = c(12.01321, 122.86239, 19.38495, 16.65468, 14.47046, 7.09872, 
33.30936, 16.92771, 21.29615, 25.93761, 21.29615, 10.10202, 24.02642, 
27.02972, 5.18752, 2.18422, 10.64807, 37.40477, 171.46128, 8.46385, 
18.01982, 11.19413, 12.55927, 21.56917, 29.76, 7.09872, 750.55266, 
12.01321, 13.37835, 16.65468, 31.94422, 5.18752, 6.27963, 31.12514, 
4.09541, 10.9211, 7.09872, 10.37505, 2.18422, 15.01651, 3.0033, 
14.74349, 3.0033, 8.19083, 19.93101, 26.21064, 14.19743, 12.83229, 
16.38165, 27.84881, 7.09872, 13.65138, 14.74349, 4.36844, 82.18128, 
45.04954, 11.46716, 13.65138, 10.64807, 24.8455, 8.73688, 341.83046, 
2.18422, 12.55927, 13.10532, 33.30936, 10.64807, 6.55266, 24.8455, 
24.29945, 13.65138, 144.43156, 66.34569, 22.38826, 4.64147, 8.46385, 
10.9211, 4.36844, 12.28624, 12.28624, 13.10532, 98.28991, 8.19083, 
5.18752, 30.85211, 34.6745, 9.00991),

X = c(0.24717402298851, -6.8443959770115, 4.78221402298851, 13.4731640229885, 
-12.0587559770115, -1.87637597701148, -5.06054597701149, 13.6288440229885, 
-12.5906659770115, -7.28763597701148, 4.07600402298851, -7.59066597701149, 
8.40933402298851, -14.8633959770115, -4.25733597701149, -7.59066597701149, 
3.03433402298851, -3.34408597701150, -10.6675859770115, 3.31842402298851, 
3.39294402298851, -0.0906659770114899, -8.17890597701149, 2.76021402298851, 
3.67917402298851, -15.3684459770115, -11.4763459770115, 4.07600402298851, 
3.03433402298851, 4.83357402298851, -3.84066597701149, -2.80805597701148, 
20.1016440229885, -2.06434597701149, -6.8214359770115, -3.10790597701149, 
5.26647402298851, -4.25733597701149, 5.26647402298851, -2.80805597701148, 
29.0760040229885, 9.4681540229885, -26.4795559770115, 4.71702402298851, 
-2.17399597701149, 6.47713402298851, 2.88552402298851, 8.3552840229885, 
7.47975402298851, -6.34066597701149, -1.22702597701149, -16.3785459770115, 
4.26980402298851, -9.81288597701148, -8.8672659770115, 1.50024402298851, 
-5.59066597701149, -11.1200759770115, 2.40933402298851, -2.59066597701149, 
24.3140940229885, -11.5430959770115, 7.8638840229885, 1.80327402298851, 
12.4093340229885, -10.0044559770115, 0.34036402298851, 8.2426640229885, 
-5.0325259770115, -6.2473859770115, 23.9477940229885, -7.9514859770115, 
-4.46566597701149, -7.59066597701149, 19.5521940229885, 31.6401040229885, 
9.63155402298851, 0.87087402298851, -2.29654597701148, -11.1200759770115, 
-2.59066597701149, -5.53491597701149, -6.34066597701149, 12.4093340229885, 
-3.59066597701149, -10.7089459770115, 7.40933402298851)
)


###############################################################
## 3.2 Local Areal Smoothing and Boundary Detection (L & C)  ##
###############################################################

model{
for(i in 1: n.area){
Y[i] ~ dpois(mu[i])
log(mu[i]) <- log(E[i]) + gamma[1] + gamma[2] * X[i] + theta[i]
RR[i] <- mu[i] / E[i]
}

# CAR prior for spatial random effects.

for (i in 1:sumNumNeigh) {weights[i]<-1}
theta[1:n.area]~car.normal(adj[], weights[], num[], tau)

# calculate "boundary likelihood values"

for(i in 1:n.edge){
delta[i] <- abs(RR[ind1[i]] - RR[ind2[i]])
deltamy[i] <-RR[ind1[i]] - RR[ind2[i]]
 }

gamma[1] ~ dflat()
gamma[2] ~ dflat()
tau ~ dgamma(0.01, 0.01)
}

## Inits:
list(tau=10, 
gamma=c(0, 0), theta=c( 0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 ))

list(tau=1, 
gamma=c(1, -1), theta=c( 10,10,10,10,10,10,10,10,10,10, 10,10,10,10,
10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,
10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,
10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,
10,10,10,10))


## Data: Y=  Breast Cancer late detection, 
## X= centered pct respodente NOT having mamgram, mean(X)= 37.59067

list( n.area=87,  n.edge=211,
num=c(8, 6, 7, 9, 4, 4, 7, 6, 3, 5, 8, 5, 4, 4, 6, 1, 5, 4, 6, 4, 
5, 4, 4, 4, 5, 5, 6, 2, 5, 5, 5, 3, 4, 6, 2, 4, 4, 2, 3, 6, 3, 
4, 4, 5, 4, 5, 5, 7, 6, 4, 5, 5, 3, 4, 6, 7, 5, 4, 3, 6, 5, 4, 
2, 6, 9, 6, 2, 4, 6, 6, 6, 5, 9, 4, 5, 6, 6, 4, 3, 5, 6, 4, 4, 
4, 4, 6, 6),
adj=c(9, 11, 18, 31, 33, 48, 58, 72, 13, 27, 30, 62, 70, 82, 14, 
15, 29, 43, 54, 56, 80, 11, 15, 29, 31, 36, 39, 44, 57, 68, 48, 
49, 70, 73, 37, 75, 76, 78, 8, 22, 40, 45, 52, 81, 83, 7, 17, 
52, 64, 65, 83, 1, 58, 72, 27, 46, 69, 71, 86, 1, 4, 18, 29, 
31, 49, 77, 80, 34, 37, 65, 76, 87, 2, 30, 58, 82, 3, 54, 56, 
84, 3, 4, 29, 43, 57, 60, 38, 8, 32, 51, 64, 83, 1, 11, 48, 49, 
25, 27, 62, 66, 69, 82, 25, 50, 55, 74, 26, 56, 61, 73, 77, 7, 
24, 45, 81, 28, 50, 55, 85, 22, 50, 74, 81, 19, 20, 55, 66, 79, 
21, 56, 75, 78, 84, 2, 10, 19, 62, 69, 86, 23, 85, 3, 4, 11, 
15, 80, 2, 13, 33, 48, 70, 1, 4, 11, 36, 72, 17, 45, 53, 1, 30, 
48, 58, 12, 47, 61, 65, 73, 76, 44, 68, 4, 31, 39, 72, 6, 12, 
76, 87, 16, 72, 4, 36, 68, 7, 52, 66, 69, 71, 81, 42, 59, 87, 
41, 51, 64, 87, 3, 15, 54, 60, 4, 35, 57, 60, 68, 7, 22, 32, 
83, 10, 47, 65, 71, 86, 34, 46, 65, 73, 86, 1, 5, 18, 30, 33, 
49, 70, 5, 11, 18, 48, 73, 77, 20, 23, 24, 55, 17, 42, 53, 59, 
64, 7, 8, 40, 65, 71, 32, 51, 67, 3, 14, 43, 60, 20, 23, 25, 
50, 79, 85, 3, 14, 21, 26, 77, 80, 84, 4, 15, 44, 60, 63, 1, 
9, 13, 33, 41, 51, 67, 15, 43, 44, 54, 57, 63, 21, 34, 73, 75, 
76, 2, 19, 27, 82, 57, 60, 8, 17, 42, 51, 65, 87, 8, 12, 34, 
46, 47, 52, 64, 71, 87, 19, 25, 40, 69, 74, 81, 53, 59, 4, 35, 
39, 44, 10, 19, 27, 40, 66, 71, 2, 5, 30, 48, 73, 86, 10, 40, 
46, 52, 65, 69, 1, 9, 31, 36, 38, 5, 21, 34, 47, 49, 61, 70, 
77, 86, 20, 24, 66, 81, 6, 26, 61, 76, 78, 6, 12, 34, 37, 61, 
75, 11, 21, 49, 56, 73, 80, 6, 26, 75, 84, 25, 55, 85, 3, 11, 
29, 56, 77, 7, 22, 24, 40, 66, 74, 2, 13, 19, 62, 7, 8, 17, 45, 
14, 26, 56, 78, 23, 28, 55, 79, 10, 27, 46, 47, 70, 73, 12, 37, 
41, 42, 64, 65),
sumNumNeigh=422,

ind1=c(1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 
3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 
7, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 10, 10, 10, 10, 10, 11, 11, 
11, 11, 11, 11, 12, 12, 12, 12, 12, 13, 13, 13, 14, 14, 14, 15, 
15, 15, 15, 16, 17, 17, 17, 17, 18, 18, 19, 19, 19, 19, 19, 19, 
20, 20, 20, 20, 21, 21, 21, 21, 21, 22, 22, 22, 23, 23, 23, 23, 
24, 24, 24, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 28, 29, 30, 
30, 30, 31, 31, 32, 32, 33, 33, 34, 34, 34, 34, 34, 35, 35, 36, 
36, 37, 37, 38, 39, 40, 40, 40, 40, 40, 41, 41, 41, 42, 42, 42, 
43, 43, 44, 44, 44, 45, 46, 46, 46, 46, 47, 47, 47, 48, 48, 49, 
49, 50, 51, 51, 51, 52, 52, 53, 54, 55, 55, 56, 56, 56, 57, 57, 
59, 60, 61, 61, 61, 62, 64, 64, 65, 65, 66, 66, 66, 69, 70, 70, 
73, 73, 74, 75, 75, 77, 78, 79),

ind2=c(9, 11, 18, 31, 33, 48, 58, 72, 13, 27, 30, 62, 70, 82, 14, 
15, 29, 43, 54, 56, 80, 11, 15, 29, 31, 36, 39, 44, 57, 68, 48, 
49, 70, 73, 37, 75, 76, 78, 8, 22, 40, 45, 52, 81, 83, 17, 52, 
64, 65, 83, 58, 72, 27, 46, 69, 71, 86, 18, 29, 31, 49, 77, 80, 
34, 37, 65, 76, 87, 30, 58, 82, 54, 56, 84, 29, 43, 57, 60, 38, 
32, 51, 64, 83, 48, 49, 25, 27, 62, 66, 69, 82, 25, 50, 55, 74, 
26, 56, 61, 73, 77, 24, 45, 81, 28, 50, 55, 85, 50, 74, 81, 55, 
66, 79, 56, 75, 78, 84, 62, 69, 86, 85, 80, 33, 48, 70, 36, 72, 
45, 53, 48, 58, 47, 61, 65, 73, 76, 44, 68, 39, 72, 76, 87, 72, 
68, 52, 66, 69, 71, 81, 42, 59, 87, 51, 64, 87, 54, 60, 57, 60, 
68, 83, 47, 65, 71, 86, 65, 73, 86, 49, 70, 73, 77, 55, 53, 59, 
64, 65, 71, 67, 60, 79, 85, 77, 80, 84, 60, 63, 67, 63, 73, 75, 
76, 82, 65, 87, 71, 87, 69, 74, 81, 71, 73, 86, 77, 86, 81, 76, 
78, 80, 84, 85),

Y = c(14, 121, 25, 18, 17, 2, 32, 20, 15, 28, 27, 8, 26, 27, 8, 2, 
10, 38, 170, 12, 19, 12, 11, 20, 36, 5, 722, 10, 11, 23, 24, 
10, 3, 30, 3, 10, 8, 13, 3, 16, 2, 10, 6, 11, 20, 29, 20, 20, 
18, 23, 5, 15, 12, 6, 76, 33, 14, 15, 7, 30, 7, 328, 2, 11, 12, 
42, 8, 13, 19, 27, 18, 143, 65, 31, 3, 13, 11, 5, 10, 14, 16, 
100, 8, 8, 38, 35, 8),

E = c(12.01321, 122.86239, 19.38495, 16.65468, 14.47046, 7.09872, 
33.30936, 16.92771, 21.29615, 25.93761, 21.29615, 10.10202, 24.02642, 
27.02972, 5.18752, 2.18422, 10.64807, 37.40477, 171.46128, 8.46385, 
18.01982, 11.19413, 12.55927, 21.56917, 29.76, 7.09872, 750.55266, 
12.01321, 13.37835, 16.65468, 31.94422, 5.18752, 6.27963, 31.12514, 
4.09541, 10.9211, 7.09872, 10.37505, 2.18422, 15.01651, 3.0033, 
14.74349, 3.0033, 8.19083, 19.93101, 26.21064, 14.19743, 12.83229, 
16.38165, 27.84881, 7.09872, 13.65138, 14.74349, 4.36844, 82.18128, 
45.04954, 11.46716, 13.65138, 10.64807, 24.8455, 8.73688, 341.83046, 
2.18422, 12.55927, 13.10532, 33.30936, 10.64807, 6.55266, 24.8455, 
24.29945, 13.65138, 144.43156, 66.34569, 22.38826, 4.64147, 8.46385, 
10.9211, 4.36844, 12.28624, 12.28624, 13.10532, 98.28991, 8.19083, 
5.18752, 30.85211, 34.6745, 9.00991),

X = c(0.24717402298851, -6.8443959770115, 4.78221402298851, 13.4731640229885, 
-12.0587559770115, -1.87637597701148, -5.06054597701149, 13.6288440229885, 
-12.5906659770115, -7.28763597701148, 4.07600402298851, -7.59066597701149, 
8.40933402298851, -14.8633959770115, -4.25733597701149, -7.59066597701149, 
3.03433402298851, -3.34408597701150, -10.6675859770115, 3.31842402298851, 
3.39294402298851, -0.0906659770114899, -8.17890597701149, 2.76021402298851, 
3.67917402298851, -15.3684459770115, -11.4763459770115, 4.07600402298851, 
3.03433402298851, 4.83357402298851, -3.84066597701149, -2.80805597701148, 
20.1016440229885, -2.06434597701149, -6.8214359770115, -3.10790597701149, 
5.26647402298851, -4.25733597701149, 5.26647402298851, -2.80805597701148, 
29.0760040229885, 9.4681540229885, -26.4795559770115, 4.71702402298851, 
-2.17399597701149, 6.47713402298851, 2.88552402298851, 8.3552840229885, 
7.47975402298851, -6.34066597701149, -1.22702597701149, -16.3785459770115, 
4.26980402298851, -9.81288597701148, -8.8672659770115, 1.50024402298851, 
-5.59066597701149, -11.1200759770115, 2.40933402298851, -2.59066597701149, 
24.3140940229885, -11.5430959770115, 7.8638840229885, 1.80327402298851, 
12.4093340229885, -10.0044559770115, 0.34036402298851, 8.2426640229885, 
-5.0325259770115, -6.2473859770115, 23.9477940229885, -7.9514859770115, 
-4.46566597701149, -7.59066597701149, 19.5521940229885, 31.6401040229885, 
9.63155402298851, 0.87087402298851, -2.29654597701148, -11.1200759770115, 
-2.59066597701149, -5.53491597701149, -6.34066597701149, 12.4093340229885, 
-3.59066597701149, -10.7089459770115, 7.40933402298851)

)


#####################################################################
# 3.3 Local Edge Smoothing and Boundary Detection      		    #
# Edge 79 and Edge 143 have no neighbors	     		    #
# Need to give independent normal priors for the two "island" edges #
#####################################################################

model{

for (i in 1:78){
prec.D [i]<- prec * size.weight[i]
D[i] ~ dnorm(eta[i], prec.D[i] )
eta[i] <- gamma[1] +gamma[2] * Z[i]+ psi[i]
#eta[i] <- gamma[1] + psi[i]
for(j in 1:3){ index[j,i]<-step(eta[i]- cut[j]) }  #step(e)=1 if e>=0
}

prec.D [79]<- prec * size.weight[79]
D[79] ~ dnorm(eta[79], prec.D[79] )
eta[79] <- gamma[1] +gamma[2] * Z[79]+ psi[79] + phi[1]
#eta[79] <- gamma[1] + psi[79] +phi [1]
for (j in 1:3){ index[j,79]<-step(eta[79]- cut[j]) }  #step(e)=1 if e>=0

for (i in 80:142){
prec.D [i]<- prec * size.weight[i]
D[i] ~ dnorm(eta[i], prec.D[i] )
eta[i] <- gamma[1] +gamma[2] * Z[i]+ psi[i]
#eta[i] <- gamma[1] + psi[i]
for(j in 1:3){ index[j,i]<-step(eta[i]- cut[j]) }  #step(e)=1 if e>=0
}


prec.D [143]<- prec * size.weight[143]
D[143] ~ dnorm(eta[143], prec.D[143] )
eta[143] <- gamma[1] +gamma[2] * Z[143]+ psi[143]+ phi[2]
#eta[143] <- gamma[1] + psi[143] +phi [2]
for(j in 1:3){ index[j,143]<-step(eta[143]- cut[j]) }  #step(e)=1 if e>=0

for (i in 144:n.edge){
prec.D [i]<- prec * size.weight[i]
D[i] ~ dnorm(eta[i], prec.D[i] )
eta[i] <- gamma[1] +gamma[2] * Z[i]+ psi[i]
#eta[i] <- gamma[1] + psi[i]
for(j in 1:3){ index[j,i]<-step(eta[i]- cut[j]) }  #step(e)=1 if e>=0
}

# CAR prior for the edge random effects

for (i in 1:sumNumNeigh) {weights[i]<-1}
psi[1:n.edge]~car.normal(adj[], weights[], num[], lambda)

for(i in 1:2) {phi[i] ~dnorm(0,lambda)}

prec ~ dgamma(0.01,0.01)
gamma[1] ~ dflat()
gamma[2] ~ dnorm(0,0.01)
lambda ~ dgamma(0.01, 0.01)
}

#Data:

# cut correspond to c(0.2, 0.25, 0.3) difference in SMRs
# Zij= | Xi-Xj | ,  Dij= log( |Yi/Ei - Yj/Ej | )
# size.weight = Yi+Yj

list(
cut=c( -1.609438,   -1.386294, -1.203973), 
D = c(-0.77428975739606, -2.27837041832934, -1.90065581327436, -0.881710222714857, 
-0.374477298000841, -0.93347632522315, -2.70914438563603, -1.74128240019704, 
-2.32995201194317, -3.77732430132385, -0.925958435670978, -3.67688535867732, 
-2.06913799594189, -3.42476995158885, -1.23525775424885, -1.37633432210983, 
-0.760492171927486, -0.345110443219141, -2.47899128908469, -0.584950615144165, 
-1.89595963585148, -1.67633921527784, -0.7735215029203, -1.35265330574233, 
-1.11027705295645, -1.80109117875050, -1.22857044920926, -1.33869367435334, 
-1.96539588921471, -0.101866800288184, -0.957735031555261, -2.576801647498, 
-2.75402767205669, -1.63429344557634, -0.168155016876377, -1.00893790244836, 
0.226500399430046, -0.147534795328486, -1.51048079140176, -2.19553068968672, 
-2.25567303310194, -3.15190640165872, -1.97978385492326, -1.34635348486564, 
-4.13447517857058, -1.41734180941002, -2.49247874570840, -1.18532235256446, 
-1.32487554477196, -1.58575690405264, -0.930294965618346, -1.25268819211130, 
-2.14084322849112, -3.61535357432927, -1.15585751103728, -1.43114729892641, 
-2.65745891416145, -1.3786374090952, -0.808309286138661, -0.660631260075448, 
-1.77759398584111, -1.34472928485578, -2.05300471752560, -1.76066633475648, 
-1.09349666416307, -2.08959002121999, -0.295683034124084, -2.34350733998892, 
-1.20780970565524, -4.09546896375517, -2.73731970716185, -0.981929727514476, 
-1.32285556839286, -0.610163093226868, -0.328589122425956, -0.78605293897805, 
-1.13542662898915, -1.09451883887215, -1.08664222497157, -0.0114997857683769, 
-1.44908510579619, -2.76002408864718, -3.28169136298622, -0.61128141514604, 
-2.49039523878288, -1.52234354200031, -3.52269833716381, -3.44397467538013, 
-1.31144906978508, -1.48390063636114, -3.65270429588688, -1.56965445790437, 
-0.524406713272569, -0.707226459438898, -3.40700751156209, -1.04970151446047, 
-1.13361485086780, -1.37360242234973, -2.59457569284973, -3.05399711759468, 
-1.93280929555659, -2.68049871769665, -1.90456207868437, -3.13660247242027, 
-2.99655238152329, -3.01720652964332, -1.03328733023069, -2.28906361482120, 
-0.782186337435169, -1.22543877171863, -1.25564300418982, -2.97144120796284, 
-0.92695044747778, -3.56933552836429, -2.84721952781262, -0.820478371600386, 
-0.176963462967671, -6.02488980474396, -1.62337552899619, -3.04851124535328, 
-0.91812894489379, -1.14802685009274, -0.101746988837977, -1.72836189916083, 
-1.30986327508467, -1.80576274415817, -1.43221814229630, -0.0787813682187029, 
0.107764000278018, 0.0777321463801405, -0.476335860769221, -0.810007850638914, 
-1.81615621164435, -3.03255379685881, -4.14358261417857, -0.558453786672301, 
-0.49357878845321, 0.224262722861393, -0.781258785410203, -2.59790107228114, 
-0.894088265736474, -1.43107194239273, -1.33591424737399, -0.493576712958423, 
-3.40231502383411, -1.63264036362404, -1.20141638704181, -1.37415197164150, 
-1.86185371017691, -4.39561298741031, -4.76322731357079, -1.50518123178543, 
-3.64632293543616, -1.62160348741254, -1.56233611645932, -0.471101193373356, 
-0.235291664924244, -2.10301697994399, -1.99876056171446, -0.444785269775291, 
-3.62086776716686, -1.19638580443010, -1.65672752276411, -1.55056960356797, 
-2.33269794840925, -0.707150813256483, -0.846324780725549, -0.91799629000898, 
-0.777011834118688, -0.804230690840237, -2.1280149974873, -2.39069669784431, 
-2.31367895174184, -2.21122620132719, -3.05853992278156, -1.76320262784695, 
-1.6975517148668, -1.51522832675381, -2.77085122413175, -1.79561146225606, 
-2.19943159846539, -1.18124128746901, -1.29208426119097, -0.899042290919727, 
-0.211170729444674, -4.31133777459422, -1.18672563763090, -2.36537865507571, 
-1.23167486501447, -1.72307900518967, -1.86527002649637, -0.308234908825996, 
-2.84975615597341, -3.22359706234693, -4.41751980829165, -0.90909335560082, 
-3.58461028724416, -0.700814827009428, -2.08951260967522, -3.21816094442368, 
-0.590911928687439, -2.02936256375152, -2.28524669178323, -3.59329467381034, 
-3.51760848138552, -1.80925539503106, -0.116986627028215, -0.69670069862892, 
-2.0229736707761, -0.9223355143837, -0.872839291141963),


Z =c(12.83784, 3.82883, 3.59126000000001, 4.08784, 19.85447, 8.10811, 
11.36725, 8.19866000000001, 15.25373, 4.63195, 11.67797, 4.6987, 
0.59701, 1.30948000000001, 19.64561, 9.03955, 1.74788, 31.26177, 
14.5951, 3.28197, 15.90229, 9.39716, 17.7305, 10.43883, 17.31383, 
16.58107, 8.20668999999999, 8.75614, 19.06383, 5.2305, 20.41404, 
19.53851, 5.81137, 7.59309000000001, 7.14284999999999, 21.42857, 
33.51648, 2.74724999999999, 18.68939, 4.96988, 2.25249000000001, 
2.88655, 11.318, 2.46988, 1.28012, 10.59451, 30.00739, 11.82557, 
1.21951, 19.96951, 1.47059, 4.63918, 4.18871000000002, 13.76477, 
2.25510999999998, 31.23543, 3.42131000000002, 7.42009000000001, 
1.04167, 7.91667, 3.40375, 5.55555, 15.19608, 5.52632, 12.85714, 
20, 39.23077, 15, 3.57576, 19.52941, 13.94425, 5.05051000000002, 
16.36364, 27.27273, 7.29167, 22.22222, 1.33333, 1.66667, 3.33333, 
5.84238999999999, 4.26136, 1.23106, 9.375, 11.69937, 10.82384, 
14.34676, 0.80876, 0.87551, 0.663130000000001, 5.63506, 5.13267000000001, 
0.36075, 9.65909, 12.18569, 10.90909, 18.76139, 1.8927, 20.92115, 
7.85861, 6.23861, 2.85088, 2.08333, 2.5, 12.25491, 1.83824, 0.68836000000001, 
4.58824, 9.10088, 10.35088, 5.35088, 12.54644, 13.68363, 5.97571999999999, 
16.86869, 34.92064, 16.23932, 27.77778, 0.0667500000000008, 6.44382, 
0.7674, 7.66667, 14.15441, 15.26807, 3.52171, 11.08096, 0.73276, 
4.11082000000001, 0.63405999999999, 7.07785999999999, 11.74636, 
31.22172, 4.94987, 26.37844, 14.47368, 2.40132, 33.70445, 11.53846, 
15.0641, 8.37438, 4.84358000000001, 26.37363, 2.14286, 3.69415000000001, 
2.97618999999999, 13.57049, 7.19640000000002, 2.22447000000002, 
26.75585, 0.21738999999999, 19.60785, 26.66667, 21.66667, 10.69518, 
7.66487999999999, 2.05881999999999, 16.66667, 23.88889, 10.30769, 
7.30769, 3.52563999999999, 4.16667, 3.59161, 5.93219999999999, 
17.47066, 17.18608, 9.52381, 7.35119, 13.59447, 0.87553, 14.60267, 
11.94542, 2.1518, 2.52660000000001, 5.49683, 3.63636, 3.0303, 
28.78788, 40.32634, 3.92944, 7.22221999999999, 6.57072000000002, 
5.27660000000001, 8.13131, 12.62032, 10.90909, 3, 13.45455, 2.06897, 
10.45455, 28.77976, 4.7619, 7.32601, 6.00818000000001, 10.60606, 
5.60606, 11.53846, 4.99999999999999, 4.97193, 2.41379000000001, 
7.41379000000001, 28.98032, 1.78172000000001, 4.46156, 14.09722, 
6.24328000000001, 5, 12.08791, 18.68132, 20.75163, 11.53846, 
1.29412000000001),


size.weight=c(29, 41, 52, 38, 17, 34, 29, 157, 147, 843, 144, 449, 148, 221, 
52, 33, 36, 31, 31, 58, 39, 45, 26, 29, 42, 28, 21, 29, 32, 31, 
37, 35, 44, 82, 10, 5, 15, 7, 52, 44, 48, 52, 47, 48, 40, 30, 
35, 31, 32, 28, 30, 158, 750, 57, 47, 46, 63, 65, 38, 51, 45, 
38, 41, 38, 16, 20, 21, 16, 49, 41, 126, 33, 60, 35, 19, 14, 
22, 38, 15, 20, 15, 21, 18, 58, 56, 206, 892, 498, 212, 189, 
270, 48, 35, 88, 43, 24, 52, 26, 84, 30, 32, 32, 28, 21, 34, 
87, 49, 43, 51, 36, 112, 78, 46, 38, 8, 10, 13, 1050, 741, 757, 
48, 25, 26, 43, 50, 34, 167, 30, 22, 23, 18, 50, 37, 42, 95, 
43, 14, 16, 13, 153, 21, 16, 156, 16, 31, 58, 35, 34, 32, 12, 
9, 10, 15, 21, 18, 12, 36, 25, 41, 24, 28, 49, 41, 47, 64, 32, 
85, 55, 38, 47, 83, 29, 99, 17, 12, 16, 27, 33, 20, 36, 86, 114, 
44, 47, 41, 44, 16, 15, 32, 72, 10, 20, 428, 23, 19, 30, 20, 
61, 73, 58, 37, 92, 62, 76, 100, 47, 16, 8, 25, 13, 48),

n.edge = 211,
sumNumNeigh = 778,
num = c(4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 2, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 3, 2, 2, 4, 2, 4, 4, 4, 4, 0, 4, 4, 4, 4, 
4, 4, 2, 4, 4, 4, 4, 2, 4, 4, 4, 3, 4, 4, 4, 4, 4, 2, 2, 4, 2, 
2, 4, 4, 1, 5, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 5, 2, 4, 5, 4, 4, 
4, 4, 2, 2, 4, 4, 4, 4, 3, 4, 4, 3, 2, 2, 2, 4, 2, 0, 2, 4, 4, 
4, 4, 4, 4, 3, 2, 4, 4, 4, 4, 4, 4, 2, 4, 5, 4, 4, 4, 4, 4, 3, 
4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 3, 2, 4, 4, 4, 4, 4, 6, 2, 3, 
2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 
2, 2),
adj = c(
7, 8, 51, 52, 
3, 4, 58, 60,
2, 6, 58, 84, 
2, 8, 60, 127, 
6, 7, 130, 131, 
3, 5, 84, 130, 
1, 5, 51, 131, 
1, 4, 52, 127, 
11,14, 69, 71, 
12, 13, 118, 120, 203, 
9, 13, 69, 125, 
10, 14, 118, 193, 
10, 11, 120, 125, 203, 
9, 12, 71, 193, 
19, 20, 72, 73, 
17,18, 75, 76, 
16, 21, 75, 122, 
16, 19, 76, 156, 
15, 18, 72, 156, 
15, 21, 73, 184, 
17, 20, 122, 184, 
24, 25, 59, 60, 
24, 29, 75, 77, 
22, 23, 59, 75, 
22, 26, 60, 126, 
25, 27, 126, 139, 
26, 30, 139, 144, 
29, 30, 158, 160, 
23, 28, 77, 158, 
27, 28, 144, 160, 
32, 33, 169, 170, 31, 34, 169, 171, 31, 34, 170, 202, 32, 33, 
171, 202, 37, 141, 37, 38, 207, 208, 35, 36, 141, 207, 36, 208, 
43, 45, 48, 50, 42, 44, 102, 103, 43, 44, 145, 149, 40, 45, 102, 
161, 39, 41, 48, 145, 40, 41, 103, 149, 39, 42, 50, 161, 47, 
50, 82, 83, 46, 49, 82, 194, 39, 43, 49, 177, 47, 48, 177, 194, 
39, 45, 46, 83, 1, 7, 1, 8, 55, 57, 119, 120, 56, 57, 164, 165, 
53, 56, 119, 201, 54, 55, 164, 201, 53, 54, 120, 165, 2, 3, 61, 
85, 22, 24, 63, 122, 2, 4, 22, 25, 58, 62, 85, 172, 61, 63, 172, 
209, 59, 62, 122, 209, 66, 67, 134, 136, 67, 68, 141, 142, 64, 
68, 137, 197, 64, 65, 136, 141, 65, 66, 142, 197, 9, 11, 70, 
123, 69, 123, 131, 9, 14, 15, 19, 15, 20, 74, 185, 73, 185, 16, 
17, 23, 24, 16, 18, 78, 157, 23, 29, 78, 186, 76, 77, 157, 186, 
81, 83, 128, 129, 80, 82, 174, 176, 46, 47, 81, 176, 46, 50, 
80, 161, 3, 6, 85, 169, 58, 61, 84, 169, 89, 112, 88, 90, 118, 
119, 87, 91, 118, 193, 86, 90, 112, 198, 87, 89, 119, 198, 88, 
193, 94, 111, 112, 199, 94, 95, 109, 173, 92, 93, 111, 173, 93, 
109, 199, 97, 98, 114, 115, 96, 100, 114, 183, 96, 99, 190, 191, 
98, 100, 190, 204, 97, 99, 183, 204, 103, 110, 40, 42, 40, 44, 
101, 110, 107, 121, 106, 173, 105, 107, 173, 182, 104, 106, 121, 
182, 109, 93, 95, 108, 110, 206, 101, 103, 109, 206, 92, 94, 
113, 181, 86, 89, 92, 199, 111, 181, 96, 97, 117, 185, 96, 116, 
191, 208, 115, 117, 208, 210, 114, 116, 185, 210, 10, 12, 87, 
88, 53, 55, 87, 90, 10, 13, 53, 57, 203, 104, 107, 17, 21, 59, 
63, 69, 70, 124, 130, 131, 123, 125, 130, 170, 11, 13, 124, 170, 
25, 26, 127, 140, 4, 8, 126, 140, 80, 161, 80, 174, 5, 6, 123, 
124, 5, 7, 70, 123, 134, 135, 166, 167, 135, 136, 190, 192, 64, 
132, 166, 132, 133, 161, 190, 64, 67, 133, 192, 66, 138, 160, 
137, 160, 26, 27, 126, 127, 35, 37, 65, 67, 65, 68, 27, 30, 41, 
43, 148, 178, 147, 149, 198, 200, 146, 148, 198, 201, 145, 147, 
178, 201, 41, 44, 146, 200, 151, 152, 153, 155, 150, 153, 175, 
150, 155, 150, 151, 154, 176, 153, 155, 176, 195, 150, 152, 154, 
195, 18, 19, 157, 180, 76, 78, 156, 180, 28, 29, 159, 186, 158, 
186, 28, 30, 137, 138, 42, 45, 83, 128, 135, 163, 165, 166, 168, 
162, 164, 166, 196, 54, 56, 163, 196, 54, 57, 162, 168, 132, 
134, 162, 163, 132, 168, 205, 162, 165, 167, 205, 31, 32, 84, 
85, 31, 33, 124, 125, 32, 34, 172, 204, 61, 62, 171, 204, 93, 
94, 105, 106, 81, 129, 175, 179, 188, 151, 174, 179, 188, 81, 
82, 153, 154, 48, 49, 178, 196, 145, 148, 177, 196, 174, 175, 
188, 156, 157, 111, 113, 182, 211, 106, 107, 181, 211, 97, 100, 
184, 209, 20, 21, 183, 209, 73, 74, 114, 117, 77, 78, 158, 159, 
187, 189, 186, 189, 174, 175, 179, 186, 187, 98, 99, 133, 135, 
98, 115, 192, 207, 133, 136, 191, 207, 12, 14, 88, 91, 47, 49, 
195, 197, 154, 155, 194, 197, 163, 164, 177, 178, 66, 68, 194, 
195, 89, 90, 146, 147, 92, 95, 112, 200, 206, 146, 149, 199, 
206, 55, 56, 147, 148, 33, 34, 203, 205, 10, 13, 120, 202, 205, 
99, 100, 171, 172, 167, 168, 202, 203, 109, 110, 199, 200, 36, 
37, 191, 192, 36, 38, 115, 116, 62, 63, 183, 184, 116, 117, 181, 
182)
)
#Data_Abc_ED.txt

#Initials:
list(psi=c(10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, NA, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, NA, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10), phi=c(10,10),
prec= 10,  gamma=c(1,0), lambda = 1)

list(psi=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0)
, phi=c(0,0),
prec=1, gamma=c(0,1), lambda=10)