我得到错误。Inprod()
中的不一致参数是什么意思?Error in jags.model(model.file, data = data, inits = init.values, n.chains = n.chains, :
RUNTIME ERROR:
Non-conforming parameters in function inprod
#Jags code
jagsscript <- cat("
model {
# Estimate the initial state vector
for(i in 1:14) {
X[i,1] ~ dnorm(0,1) # weakly informative normal prior
xknot[i] <- X[i,1]
}
# B matrix of interactions
for (r in 3:14){
B[r,1] <- 0
}
for (r in c(2:14)){
B[r,2] <- 0
}
for (r in c(1:2, 4:14)){
B[r,3] <- 0
}
for (r in c(1:3, 5:14)){
B[r,4] <- 0
}
for (r in c(1:4, 6:14)){
B[r,5] <- 0
}
for (r in c(1:5, 7:14)){
B[r,6] <- 0
}
for (r in c(1:6, 8:14)){
B[r,7] <- 0
}
for (r in c(1:7, 9:14)){
B[r,8] <- 0
}
for (r in c(1:8, 10:14)){
B[r,9] <- 0
}
for (r in c(1:9, 11:14)){
B[r,10] <- 0
}
for (r in c(1:10, 12:14)){
B[r,11] <- 0
}
for (r in c(1:11, 13:14)){
B[r,12] <- 0
}
for (r in c(1:12, 14)){
B[r,13] <- 0
}
for (r in c(1:13)){
B[r,14] <- 0
}
B[1, 1] <- phi[1]
B[1, 2] <- phi[2]
B[2, 1] <- 1
for (n in 3:12){
B[n,n] <- rho[n-2]
}
B[13, 13] <- 1
B[14, 14] <- 1
for (n in 1:10){
rho[n] ~ dunif(-1, 1)
}
phi[1] ~ dnorm(0, 1.3)
phi[2] ~ dnorm(0, 1.3)
for (r in 1:10){
Z[r,2] <- 0
}
for (r in 2:11){
Z[r,3] <- 0
}
for (r in c(1, 3:11)){
Z[r,4] <- 0
}
for (r in c(1,2, 4:11)){
Z[r,5] <- 0
}
for (r in c(1:3, 5:11)){
Z[r,6] <- 0
}
for (r in c(1:4, 6:11)){
Z[r,7] <- 0
}
for (r in c(1:5, 7:11)){
Z[r,8] <- 0
}
for (r in c(1:6, 8:11)){
Z[r,9] <- 0
}
for (r in c(1:7, 9:11)){
Z[r,10] <- 0
}
for (r in c(1:8, 10:11)){
Z[r,11] <- 0
}
Z[11,1] <- beta[1]
Z[11,2] <- beta[2]
Z[1, 1] <- ld[1]
Z[2, 1] <- ld[2]
Z[3, 1] <- ld[3]
Z[4, 1] <- ld[4]
Z[5, 1] <- ld[5]
Z[6, 1] <- ld[6]
Z[7, 1] <- ld[7]
Z[8, 1] <- ld[8]
Z[9, 1] <- ld[9]
Z[10, 1] <- ld[10]
Z[11,12] <- 1
Z[1,3] <- 1
Z[2,4] <- 1
Z[3,5] <- 1
Z[4,6] <- 1
Z[5,7] <- 1
Z[6,8] <- 1
Z[7,9] <- 1
Z[8,10] <- 1
Z[9,11] <- 1
Z[10,12] <- 1
Z[11,13] <- 1
beta[1] ~ dbeta(2.21, 12.53)
beta[2] ~ dbeta(2.21, 12.53)
for (i in 1:10){
ld[i] ~ dnorm(0, 1.3)
}
# Autoregressive process
for(t in 2:nYears) {
for(i in 1:14) {
predX[i,t] <- inprod(B[i,], X[,t-1])
X[i,t] ~ dnorm(predX[i,t], tauQ[states[i]])
}
}
tauQ[1] ~ dscaled.gamma(1, 10)
Q1 <- 1 / tauQ[1]
Q2 <- 1 / tauQ[2]
Q2 <- 0
for( i in 3:14){
tauQ[i] ~ dscaled.gamma(1, 10)
}
Q3 <- 1 / tauQ[3]
Q4 <- 1 / tauQ[4]
Q5 <- 1 / tauQ[5]
Q6 <- 1 / tauQ[6]
Q7 <- 1 / tauQ[7]
Q8 <- 1 / tauQ[8]
Q9 <- 1 / tauQ[9]
Q10 <- 1 / tauQ[10]
Q11 <- 1 / tauQ[11]
Q12<- 1 / tauQ[12]
Q13 <- 1 / tauQ[13]
Q14 <- 1 / tauQ[14]
# Observation model
for(t in 1:nYears) {
for(i in 1:11) {
predY[i,t] <- inprod(Z[i,], X[,t])
Y[i,t] ~ dnorm(predY[i,t], tauR[hf[i]])
}
}
for( i in 1:11){
tauR[i] ~ dscaled.gamma(1, 10)
}
R1 <- 1 / tauR[1]
R2 <- 1 / tauR[2]
R3 <- 1 / tauR[3]
R4 <- 1 / tauR[4]
R5 <- 1 / tauR[5]
R6 <- 1 / tauR[6]
R7 <- 1 / tauR[7]
R8 <- 1 / tauR[8]
R9 <- 1 / tauR[9]
R10 <- 1 / tauR[10]
R11 <- 1 / tauR[11]
}
",file="marss-jags.txt")
#putting data
tdat <- t(hf_cycle)
jags.data <- list(Y = tdat,
nYears = ncol(tdat),
states = 1:14, hf = 1:11)
jags.params <- c("ld", "rho", "beta","phi", "xknot", "R1", "R2", "R3", "R4", "R5", "R6", "R7", "R8", "R9", "R10", "R11",
"Q1", "Q2", "Q3", "Q4","Q5","Q6","Q7","Q8","Q9","Q10", "Q11", "Q12","Q13","Q14")
model.loc <- "marss-jags.txt"
#running jags
mod_1 <- jags(jags.data,
parameters.to.save = jags.params,
model.file = model.loc,
n.chains = 2,
n.burnin = 2000,
n.thin = 1,
n.iter = 5000)
我们正在尝试使用贝叶斯技术从高频指标估计需求指数。具体来说，是关节后未知参数和未观测因子的分布可以是抽样使用马尔科夫链蒙特卡罗程序上的全套条件分布
JAGS用户手册简要描述了第43页(表9.2)中inprod()的限制:
inprod(x1, x2)
Description: Inner product
Restrictions: Dimensions of x1, x2 conform
在你的例子中，你有:
inprod(B[i,], X[,t-1])
inprod(Z[i,], X[,t])
所以B的列数和Z的列数必须都等于x的行数。JAGS抱怨其中一个(或两个)不是这样。
为j in 1:13定义一个值Z[i,j]，因此矩阵Z有13列。但是矩阵X有14行，并且您在Z的行Z[i, ](长度为13)和X的列X[, t](长度为14)之间执行内积。