Question

我正在尝试使用以下代码（改编自Gelman和Hill的书中给出的代码）来估计Jags中的变化系数/截距有序概率模型。但是，它给我一个“观察到的节点与初始化时未观察到的父节点不一致。尝试设置适当的初始值”。我哪里错了？有人可以帮帮我吗？在此先感谢!!

rm(list=ls(all=TRUE));
options(warn=-1)
library(mvtnorm)
library(arm)
library(foreign)
library("R2jags")
library(MCMCpack)

set.seed(1)

standardizeCols = function( dataMat ) {
    zDataMat = dataMat
    for ( colIdx in 1:NCOL( dataMat ) ) {
        mCol = mean( dataMat[,colIdx] )
        sdCol = sd( dataMat[,colIdx] )
        zDataMat[,colIdx] = ( dataMat[,colIdx] - mCol ) / sdCol
    }
    return( zDataMat )
}

keep<-1
nobs = 150;
nis<-sample(1:40,nobs,replace=T)    # number obs per subject 
id<-rep(1:nobs,nis)
N<-length(id)
corr_beta = 0.6;
Sigma_beta = matrix(c(1, corr_beta, corr_beta, corr_beta, 
              corr_beta, 1, corr_beta, corr_beta,
              corr_beta, corr_beta, 1, corr_beta,
              corr_beta, corr_beta, corr_beta, 1), ncol=4);
betas <- rmvnorm(n=N, mean=c(-1.45, 0.90, 0.25, -2.3), sigma=Sigma_beta);

#Generate the data
x3 = matrix(0, nrow=N,ncol=3);
y3 = matrix(0, nrow=N,ncol=1);

for (i in 1:N) {
    error_v = rnorm(1,0,1);
    x3[i,1] = rnorm(1,0,1);
    x3[i,2] = rnorm(1,0,1);
    x3[i,3] = rnorm(1,0,1);
    y3[i,1] = betas[id[i], 1] + betas[id[i], 2]*x3[i,1] + betas[id[i], 3]*x3[i,2] + betas[id[i], 4]*x3[i,3] + error_v;
}
cutoff=c(-100, 0, 1.5, 2.4,   100)
k=length(cutoff)-1;
Y3<-cut(y3, br = cutoff, right=TRUE, include.lowest = TRUE, labels = FALSE)
Y3=Y3
X3=x3
m1=max(Y3)

y = as.vector( Y3 )
n = length(y)
J<-length(unique(id))
X =  cbind(1, standardizeCols( X3 ))
nPred = NCOL(X) 
subjects<-as.vector(as.numeric(id))
K=nPred
W <- diag (K)

# MCMC settings
ni <- 5000; nb <- 2500; nt <- 6; nc <- 3 

tau1u=c(0,1,2)
jags_data <- list ("n", "J", "K", "y", "subjects", "X", "W", "m1")

inits <- function (){
    list (B.raw=array(rnorm(J*K),c(J,K)), mu.raw=rnorm(K), sigma.y=runif(1), Tau.B.raw=rwish(K+1,diag(K)), xi=runif(K))
}

params <- c ("B", "mu", "sigma.B", "rho.B", "tau1u")

cat("model {

    for (i in 1:n){
        y.hat[i] <- inprod(B[subjects[i],],X[i,])
        y[i] ~ dcat(p[i,])
        estar[i]~dnorm (y.hat[i], tau.y);


        for (j in 1:(m1-1)) {
            Q1[i,j]<-pnorm(tau1[j]-estar[i],0,1)
        }

        p[i,1] <- Q1[i,1]
        for(j in 2:(m1-1)) {
            p[i,j] <- Q1[i,j] - Q1[i,j-1]
        }
        p[i,m1] <- 1 - Q1[i,m1-1] 
    }

    tau.y <- pow(sigma.y, -2)
    sigma.y ~ dunif (0, 100)

    # thresholds (unordered priors)
    for(j in 1:(m1-1)){
      tau1u[j] ~ dnorm(0,.01)
    }

    # ordered thresholds
    tau1 <- sort(tau1u)

    for (j in 1:J){
        for (k in 1:K){
            B[j,k] <- xi[k]*B.raw[j,k]
        }
        B.raw[j,1:K] ~ dmnorm (mu.raw[], Tau.B.raw[,])
    }
    for (k in 1:K){
        mu[k] <- xi[k]*mu.raw[k]
        mu.raw[k] ~ dnorm (0, .0001)
        xi[k] ~ dunif (0, 100)
    }

    Tau.B.raw[1:K,1:K] ~ dwish (W[,], df)
    df <- K+1
    Sigma.B.raw[1:K,1:K] <- inverse(Tau.B.raw[,])

    for (k in 1:K){
        for (k.prime in 1:K){
            rho.B[k,k.prime] <- Sigma.B.raw[k,k.prime]/sqrt(Sigma.B.raw[k,k]*Sigma.B.raw[k.prime,k.prime])
        }
        sigma.B[k] <- abs(xi[k])*sqrt(Sigma.B.raw[k,k])
    }
}", fill=TRUE, file="wishart2.txt")

# Start Gibbs sampler
outj <- jags(jags_data, inits=inits, parameters.to.save=params, model.file="wishart2.txt", n.thin=nt, n.chains=nc, n.burnin=nb, n.iter=ni)

Answer 1

您的初始值函数返回正态分布和均匀分布中的随机数，它看起来与足够接近合理值以允许计算非0后验值。我认为您需要更仔细地选择初始值，并且可能基于数据中生成的值，以确保模型编译。格尔曼和希尔能为他们的模型提供初始值吗？

更新：您还可以尝试删除'inits = inits'参数，以允许JAGS选择自己的初始值，这适用于大多数（尽管不是全部）模型。我不使用R2JAGS，所以我不确定这是否允许jags函数（但它适用于rjags和runjags）。

Jags中的有序Probit使用缩放的逆Wishart

1 个答案: