我试图估算JAGS中的模型。我希望州立大学能够依赖区域平均水平。我一直在搞乱它，除了错误之外别无其他任何东西。我无法理解为什么会发生这种情况，因为当我向法线的平均值添加一个常量时，它不会抛出任何错误（我在下面添加的1）但是当我添加另一个常量时，z [n]的我得到了节点错误。
Error in node y[8]
Node inconsistent with parents
model{
for(i in 1:N)
{
gend[i] = inprod(betaG, g[i])
eta[i] = beta0 + betaState[state[i]] + gend[i]
pi[i] = 1/(1 + exp(-eta[i]))
y[i] ~ dbern(pi[i])
}
beta0 ~dnorm(0, .001)
betaG ~ dnorm(0, .001)
for( r in 1:4){
region[r] ~ dnorm(0, .0001)
}
gamma <- c(1,1)
for(n in 1:9){
z[n] <- inprod(gamma, xState[n, 2:3])
betaState[n] ~ dnorm(region[1] + z[n], .001)
}
for(n in 10:26){
betaState[n] ~ dnorm(region[2] +1, .001)
}
for(n in 27:38){
betaState[n] ~ dnorm(region[3]+1 , .001)
}
for(n in 39:51){
betaState[n] ~ dnorm(region[4]+ 1, .001)
}
}
我已经查看了有关此错误的其他人的帖子，我无法实现发布到我的问题的答案。
答案 0 :(得分：0)
model{
for(i in 1:N)
{
asy[i] <- alphaStateYear[dataTime[i,2], state[i]]
aes[i] <- alphaEdState[ed[i], rData[i], state[i]]
aas[i] <- alphaAgeState[age[i], rData[i], state[i]]
bbs[i] <- betaBlackState[race[i,3], rData[i], state[i]]
bgs[i] <- betaGenderState[g[i,3], rData[i], state[i]]
bbfs[i] <- betaBlackFemaleState[g[i,3], race[i,3], rData[i], state[i]]
das[i] <- deltaAgeState[age[i], rData[i], state[i]]
dbs[i] <- deltaBlackState[race[i,3], rData[i], state[i]]
dgs[i] <- deltaGenderState[g[i,3], rData[i], state[i]]
dbfs[i] <- deltaBlackFemaleState[g[i,3], race[i,3], rData[i], state[i]]
eta[i] <- (asy[i] + aes[i] + aas[i]) +
(das[i] * dataTime[i, 1]) +
(bbs[i] * race[i, 2]) + (dbs[i] * race[i,2] * dataTime[i, 1]) +
(bgs[i] * g[i,2]) + (dgs[i]*g[i,2]*dataTime[i,1]) +
(bbfs[i]*race[i,2]*dataTime[i,1]) + (dbfs[i] * race[i,2] * g[i,2] * dataTime[i,1])
pi[i] = 1/(1 + exp(-eta[i]))
y[i] ~ dbern(pi[i])
}
for( r in 1:4){
alphaRegion[r] ~ dnorm(0, .0001)
deltaRegion[r] ~ dnorm(0, .0001)
sigiRegion[r] ~ dt(0,5,3)I(0,)
}
for( k in 1:51){
mu[k] <- alphaRegion[region[k]] + inprod(beta[1:2],xState[k, 1:2])
alphaState[k] ~ dnorm(mu[k], sigiRegion[region[k]])
}
beta[1] ~ dnorm(0, .04)
beta[2] ~ dnorm(0, .04)
nationalMu ~ dnorm(0, .04)
muDelta ~ dnorm(0, .04)
phi ~ dunif(-1,1)
sigma ~ dt(0, 5, 3)I(0,)
mu1[1] <- nationalMu + muDelta * timeAndIndex[1, 2]
alphaYear[1] ~ dnorm(mu1[1], pow( pow(sigma,2)/ (1- pow(phi,2)), -1))
for( t in 2:54 ){
mu1[t] <- nationalMu + muDelta * timeAndIndex[t, 2] + phi*(alphaYear[t-1] - nationalMu - muDelta * timeAndIndex[t-1, 2])
alphaYear[t] ~ dnorm(mu1[t], pow(sigma,-2))
}
sigmaStateYear ~ dt(0, 5, 3)I(0,)
for(yr in 1:54){
for(st in 1:51){
alphaStateYear[yr, st] ~ dnorm(mu2[yr, st], sigmaStateYear)
mu2[yr, st] <- alphaYear[yr] + alphaState[st] + deltaState[st] * timeAndIndex[yr, 2]
}
}
tauStateYear[1] ~ dt(0, 5, 3)I(0,)
tauStateYear[2] ~ dt(0, 5, 3)I(0,)
tauStateYear[3] ~ dt(0, 5, 3)I(0,)
tauStateYear[4] ~ dt(0, 5, 3)I(0,)
for(q in 1:51){
deltaState[q] ~ dnorm(mu3[q], tauStateYear[region[q]])
mu3[q] <- deltaRegion[region[q]] + inprod(gamma[1:2], zState[q, 1:2])
}
gamma[1] ~ dnorm(0, .04)
gamma[2] ~ dnorm(0, .04)
for( a in 1:4){
nationalAge[a] ~ dnorm(0, .04)
nationalAge1[a] ~ dnorm(0, .04)
nationalTau[a] ~ dt(0, 5, 3)I(0,)
nationalSigma[a] ~ dt(0, 5, 3)I(0,)
for(indx in 1:4){
regionalAge[a, indx] ~ dnorm(nationalAge[a], pow(nationalSigma[a],-2))
regionalAge1[a, indx] ~ dnorm(nationalAge1[a], pow(nationalTau[a],-2))
regionalTau[a, indx] ~ dt(0, 5, 3)I(0,)
regionalSigma[a, indx] ~ dt(0, 5, 3)I(0,)
for(indx2 in 1:51){
alphaAgeState[a, indx, indx2] ~ dnorm(regionalAge[a, indx], pow(regionalSigma[a, indx],-2))
deltaAgeState[a, indx, indx2] ~ dnorm(regionalAge[a, indx], pow(regionalTau[a, indx],-2))
}
}
}
for( e in 1:4){
nationalEd[e] ~ dnorm(0, .04)
nationalSigmaEd[e] ~ dt(0, 5, 3)I(0,)
for(indxA in 1:4){
regionalEd[e, indxA] ~ dnorm(nationalEd[e], pow(nationalSigmaEd[e],-2))
regionalSigmaEd[e, indxA] ~ dt(0, 5, 3)I(0,)
for(indxB in 1:51){
alphaEdState[e, indxA, indxB] ~ dnorm(regionalEd[e, indxA], pow(regionalSigmaEd[e, indxA],-2))
}
}
}
for(blackNonBlack in 1:2){
blackGrandMean[blackNonBlack] ~ dnorm(0, sig)
deltaGrandMean[blackNonBlack] ~ dnorm(0, sig)
nationalSigmaBlack[blackNonBlack] ~ dt(0, 5, 3)I(0,)
nationalTauBlack[blackNonBlack] ~ dt(0, 5, 3)I(0,)
for(reg in 1:4){
blackReg[blackNonBlack, reg] ~ dnorm(blackGrandMean[blackNonBlack], nationalSigmaBlack[blackNonBlack])
deltaReg[blackNonBlack, reg] ~ dnorm(deltaGrandMean[blackNonBlack], nationalTauBlack[blackNonBlack])
regionalSigmaBlack[blackNonBlack, reg] ~ dt(0, 5, 3)I(0,)
regionalTauBlack[blackNonBlack, reg] ~ dt(0, 5, 3)I(0,)
for(sta in 1:51){
betaBlackState[blackNonBlack, reg, sta] ~ dnorm(blackReg[blackNonBlack, reg], regionalSigmaBlack[blackNonBlack, reg])
deltaBlackState[blackNonBlack, reg, sta] ~ dnorm(deltaReg[blackNonBlack, reg], regionalTauBlack[blackNonBlack, reg])
}
}
}
for(gender in 1:2){
genderGrandMean[gender] ~ dnorm(0 , .04)
deltaGenderGrandMean[gender] ~ dnorm(0, .04)
nationalGenderSigma[gender] ~ dt(0, 5, 3)I(0,)
nationalGenderTau[gender] ~ dt(0, 5, 3)I(0,)
for(regi in 1:4){
genderReg[gender, regi] ~ dnorm(genderGrandMean[gender], nationalGenderSigma[gender])
deltaGenderReg[gender, regi] ~ dnorm(deltaGenderGrandMean[gender], sig)
regionalGenderSigma[gender, regi] ~ dt(0, 5, 3)I(0,)
regionalGenderTau[gender, regi] ~ dt(0, 5, 3)I(0,)
for(place in 1:51){
betaGenderState[gender, regi, place] ~ dnorm(genderReg[gender, regi], regionalGenderSigma[gender, regi])
deltaGenderState[gender, regi, place] ~ dnorm(deltaGenderReg[gender, regi], regionalGenderTau[gender, regi])
}
}
}
sig <- .1
for (gen in 1:2){
ggm[gen] ~ dnorm(0, sig)
dggm[gen] ~ dnorm(0, sig)
nationalGenSigma[gen] ~ dt(0, 5, 3)I(0,)
nationalGenTau[gen] ~ dt(0, 5, 3)I(0,)
for(ra in 1:2){
imBlack[gen, ra] ~ dnorm(ggm[gen], nationalGenSigma[gen])
deltaImBlack[gen, ra] ~ dnorm(dggm[gen], nationalGenTau[gen])
genderRaceSigma[gen, ra] ~ dt(0, 5, 3)I(0,)
genderRaceTau[gen, ra] ~ dt(0, 5, 3)I(0,)
for(part in 1:4){
imBlackInPart[gen, ra, part] ~ dnorm(imBlack[gen, ra], genderRaceSigma[gen, ra])
deltaImBlackPart[gen, ra, part] ~ dnorm(deltaImBlack[gen, ra], genderRaceTau[gen, ra])
genderRaceRegionSigma[gen, ra, part] ~ dt(0, 5, 3)I(0,)
genderRaceRegionTau[gen, ra, part] ~ dt(0, 5, 3)I(0,)
for(estado in 1:51){
betaBlackFemaleState[gen, ra, part, estado] ~ dnorm(imBlackInPart[gen, ra, part], genderRaceRegionSigma[gen, ra, part])
deltaBlackFemaleState[gen, ra, part, estado] ~ dnorm(deltaImBlackPart[gen, ra, part], genderRaceRegionTau[gen, ra, part])
}
}
}
}
}