我目前正在开发一个关于双变量正态分布的OpenBUGS代码。该分布使用Wishart先验精度,我在更新模型时遇到了一些麻烦。
我的模型将加载,并且它会使用我的数据进行编译。但是,在更新时,我收到一条错误消息“TRAP 126(尚未实现)”,它与tau.org [1,1]有关。
我认为问题在于Wishart之前,但我不确定如何解决它。我尝试使用相同的先前更简单的模型,并且没有更新问题,我意识到问题可能是由eps_theta引起的。
我已经检查过大多数来源,但错误仍然存在。如果有人能够对这个问题有所了解,那就太棒了。提前感谢!
model {
for (i in 1:10) {
y[i,1:2] ~ dmnorm(loc[i,1:2] , tau.t[i, ,])
mu[i,1] <- alpha[1] + beta[2]*x[i] #Eliminate 'j' due to theta
mu[i,2] <- alpha[2] + beta[2]*x[i]
skew[i,1] <- lambda[i]*eps_theta[1]
skew[i,2] <- lambda[i]*eps_theta[2]
loc[i,1] <- mu[i,1] + skew[i,1]
loc[i,2] <- mu[i,2] + skew[i,2]
} #close i loop
eps_theta[1] <- cov.org[1,1]*theta[1] + cov.org[1,2]*theta[2]
eps_theta[2] <- cov.org[2,1]*theta[1] + cov.org[2,2]*theta[2]
# tau.t
for (i in 1:10){
tau.t[i,1,1] <- inv.lambda[i]*tau.org[1,1]
tau.t[i,1,2] <- inv.lambda[i]*tau.org[1,2]
tau.t[i,2,1] <- inv.lambda[i]*tau.org[2,1]
tau.t[i,2,2] <- inv.lambda[i]*tau.org[2,2]
inv.lambda[i] ~ dgamma(dfby2, dfby2)
lambda[i] <- 1/inv.lambda[i]
}
cov.org[1:2,1:2] <- inverse(tau.org[ , ])
for (i in 1:10){
cov.t[i,1:2,1:2] <- inverse(tau.t[i, ,])
} #Think if this is necessary
#Priors
for (j in 1:2){
alpha[j] ~ dnorm(0, 1)
beta[j] ~ dnorm(1, 1)
theta[j] ~ dunif(-30,30)
}
tau.org[1:2,1:2] ~ dwish(R[,],2) #R value provided in data
dfby2 <- df/2
df ~ dexp(0.1)I(1.01,40)
}
# Initials
list(
alpha = c(0,0),
beta = c(1,1),
df = 15,
theta = c(0,0),
tau.org = structure(
.Data = c(1,0.1,0.1,1),
.Dim=c(2,2)))
list(
alpha = c(-5,-5),
beta = c(5,5),
df = 50,
theta = c(5,5),
tau.org = structure(
.Data = c(0.1,0,0,0.1),
.Dim=c(2,2)))
list(
alpha = c(2,2),
beta = c(-1,-1),
df = 30,
theta = c(-5,-5),
tau.org = structure(
.Data = c(10,1,1,10),
.Dim=c(2,2)))
#Provide data
list(x=c(-2.6378, 0.7446, 0.5019, -0.9829, 1.2866, -0.2914, 0.5272, -4.9235, -1.5481, 0.1563),
y = structure(
.Data = c(-3.2529, -3.4632,
-1.4764, -0.2982,
2.8630, 0.7316,
-2.1607, -0.6788,
0.6701, 1.0510,
-1.3155, 0.6880,
1.0596, 0.0861,
-3.6570, -3.8032,
-1.6598, -2.7763,
3.9920, 0.3544),
.Dim = c(10,2)),
R = structure(
.Data=c(0.001,0,0,0.001),
.Dim=c(2,2)))