model{
for (i in 1:nSNP){
# likelihood
z[i] ~ dbetabin(a[mixture[i]],b[mixture[i]],N[i])
mixture[i] ~ dcat(p[1:Nclust])
}
# hyperprior
for (clustID in 1:2){
a[clustID] <- mu[clustID]*kappa
b[clustID] <- (1-mu[clustID])*kappa
p[clustID] <- 0.495
}

for (i in 1:nSites){
# likelihood
N_cov[i] ~ dnegbin(p_cov,r_cov)
}
#prior
p_cov <- r_cov/(r_cov+m_cov)
r_cov ~ dgamma(0.01,0.01)
m_cov <- m0*(2-f)/2
  
f ~ dbeta(1,1)
p[3] <- 0.01
a[3] <- 1
b[3] <- 1
kappa ~ dgamma(1,0.1)
d1 <- m*f*(1-m)/(1-f+m*f)
d2 <- m*f*(1-m)/(1-m*f)
mu[1] <- m+d1
mu[2] <- m-d2
p1 <- sum(mixture[1:nSNP]==1)/nSNP
p2 <- sum(mixture[1:nSNP]==2)/nSNP
p3 <- sum(mixture[1:nSNP]==3)/nSNP
}
