jags_2mqrjm_f_b.weib.value <-
function (...) {
for(q in 1:Q){
c1[q] <- (1-2*tau[q])/(tau[q]*(1-tau[q]))
c2[q] <- 2/(tau[q]*(1-tau[q]))
}
# likelihood
for (i in 1:I){
# longitudinal part
for(j in offset[i]:(offset[i+1]-1)){
# define object
W[j, 1] ~ dexp(1/sigma[1])
W[j, 2] ~ dexp(1/sigma[2])
V11[j] <- W[j, 1]*sigma[1]*c2[1]
V22[j] <- W[j, 2]*sigma[2]*c2[2]
V12[j] <- sqrt(V11[j]*V22[j])*rho
# first quantile distribution
y[j, 1] ~ dnorm(mu1[j], prec1[j])
mu1[j] <- inprod(beta[1, 1:ncX], X[j, 1:ncX]) + inprod(b1[i, 1:ncU], U[j, 1:ncU]) + c1[1]*W[j, 1]
prec1[j] <- 1/V11[j]
# conditional distrubtion for second quantile given y[j, 1]
y[j, 2] ~ dnorm(mu2.knowing.y1[j], prec2[j])
mu2[j] <- inprod(beta[2, 1:ncX], X[j, 1:ncX]) + inprod(b2[i, 1:ncU], U[j, 1:ncU]) + c1[2]*W[j, 2]
mu2.knowing.y1[j] <- mu2[j] + rho*sqrt(V22[j]/V11[j])*(y[j, 1]-mu1[j])
prec2[j] <- 1/(V22[j]*(1-pow(rho, 2)) )
}#end of j loop
# random effects
b1[i, 1:ncU] ~ dmnorm(mu0[], prec1.Sigma2[, ])
b2[i, 1:ncU] ~ dmnorm(mu0[], prec2.Sigma2[, ])
# survival part
etaBaseline[i] <- inprod(alpha[1: ncZ], Z[i, 1:ncZ])
shareQ1[i] <- inprod(beta[1, 1:ncX], Xtime[i, 1:ncX]) + inprod(b1[i, 1:ncU], Utime[i, 1:ncU])
shareQ2[i] <- inprod(beta[2, 1:ncX], Xtime[i, 1:ncX]) + inprod(b2[i, 1:ncU], Utime[i, 1:ncU])
log_h1[i] <- log(shape) + (shape - 1) * log(Time[i]) + etaBaseline[i] + alpha.assoc * (shareQ2[i] - shareQ1[i])
for (k in 1:K) {
shareQ1.s[i, k] <- inprod(beta[1, 1:ncX], Xs[K * (i - 1) + k, 1:ncX]) + inprod(b1[i, 1:ncU], Us[K * (i - 1) + k, 1:ncU])
shareQ2.s[i, k] <- inprod(beta[2, 1:ncX], Xs[K * (i - 1) + k, 1:ncX]) + inprod(b2[i, 1:ncU], Us[K * (i - 1) + k, 1:ncU])
SurvLong[i, k] <- wk[k] * shape * pow(st[i, k], shape - 1) * exp(alpha.assoc * (shareQ2.s[i, k] - shareQ1.s[i, k]) )
}
log_S1[i] <- (-exp(etaBaseline[i]) * P[i] * sum(SurvLong[i, ]))
logL[i] <- event[i]*log_h1[i] + log_S1[i]
mlogL[i] <- -logL[i] + C
zeros[i] ~ dpois(mlogL[i])
}#end of i loop
# priors for parameters
prec1.Sigma2[1:ncU, 1:ncU] ~ dwish(priorR.Sigma2[, ], priorK.Sigma2)
covariance.b1 <- inverse(prec1.Sigma2[, ])
prec2.Sigma2[1:ncU, 1:ncU] ~ dwish(priorR.Sigma2[, ], priorK.Sigma2)
covariance.b2 <- inverse(prec2.Sigma2[, ])
for(qqq in 1:Q){
beta[qqq, 1:ncX] ~ dmnorm(priorMean.beta[qqq, ], priorTau.beta[, ])
sigma[qqq] ~ dgamma(priorA.sigma, priorB.sigma)
}
rho ~ dunif(-1,1)
# priors for survival parameters
alpha[1:ncZ] ~ dmnorm(priorMean.alpha[], priorTau.alpha[, ])
shape ~ dgamma(priorA.shape, priorB.shape)
alpha.assoc ~ dnorm(priorMean.alpha.assoc, priorTau.alpha.assoc)
}
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