model{ for(i in 1:N){ for(j in 1:6){y2[i,j] ~ dnorm(muy2[i,j],psiy2[j])} muy2[i,1] <- xi2[i,1] muy2[i,4] <- xi2[i,2] for(j in 2:3){ muy2[i,j] <- ly2[j-1]*xi2[i,1] muy2[i,j+3] <- ly2[j+1]*xi2[i,2] } xi2[i,1:2] ~ dmnorm(muxi2[1:2], psixi2[1:2,1:2]) eta2[i] <- b2[1] + b2[2]*xi2[i,1] + b2[3]*xi2[i,2] + b2[4]*xi2[i,1]*xi2[i,2] P[i,1,1] <- exp(eta2[i])/(exp(eta2[i])+1) P[i,1,2] <- 1-P[i,1,1] P[i,2,1] <- 0 P[i,2,2] <- 1 for(t in 2:zeit){ C[i,t] ~ dcat(P[i,C[i,t-1],1:2]) } for(t in 1:zeit){ eta1[i,t] ~ dnorm(mueta1[i,t],psizeta1[C[i,t]]) for(j in 1:3){y1[i,t,j] ~ dnorm(muy1[i,t,j],psiy1[j])} muy1[i,t,1] <- eta1[i,t] for(j in 2:3){ muy1[i,t,j] <- ly1[j-1]*eta1[i,t] } } mueta1[i,1] <- b0[C[i,1]] mueta1[i,2] <- b0[C[i,2]] + rho[1]*eta1[i,1] for(t in 3:zeit){ mueta1[i,t] <- b0[C[i,t]] + rho[1]*eta1[i,t-1] + rho[2]*eta1[i,t-2] } } b2[1] ~ dunif(2,6) b0[1] <- 0 b0[2] ~ dunif(0,4) psixi2[1:2,1:2] ~ dwish(PHinv[1:2,1:2],4) for(j in 1:2){ly1[j] ~ dnorm(1,1)} for(j in 1:2){rho[j] ~ dnorm(0,1)} for(j in 1:4){ly2[j] ~ dnorm(1,1)} for(j in 2:4){b2[j] ~ dunif(0,1)} for(j in 1:2){muxi2[j] <- 0} for(i in 1:3){psiy1[i] ~ dgamma(9,4)} for(i in 1:6){psiy2[i] ~ dgamma(9,4)} for(j in 1:2){psizeta1[j] ~ dgamma(9,4)} for(j in 1:3){sigmay1[j] <- 1/psiy1[j]} for(j in 1:6){sigmay2[j] <- 1/psiy2[j]} for(j in 1:2){sigmaeta1[j] <- 1/psizeta1[j]} sigmaxi2[1:2,1:2] <- inverse(psixi2[1:2,1:2]) }