我试图在JAGS中编写最简单的缺失数据模型。 一个预测变量（具有一些缺失的数据点）和一个结果变量。 我知道这个例子不是最有用或最现实的，但是在我继续讨论更复杂的预测数据方案之前，它帮助我解决了模型问题。
模型和数据如下，但这里是编译错误：
Error in jags.model("MISSING_model.txt", data = dataList, inits = initsList, :
RUNTIME ERROR:
Unable to resolve the following parameters:
x[3] (line 5)
x[4] (line 5)
x[7] (line 5)
x[13] (line 5)
x[18] (line 5)
x[20] (line 5)
Either supply values for these nodes with the data
or define them on the left hand side of a relation.
这些是缺失的数据点;我在下面定义它们，所以我不确定错误在哪里。
代码：
# DEFINING THE DATA:
myData <- matrix (
c(64.0, 62.3, NA, NA, 64.8, 57.5, NA, 70.2, 63.9, 71.1,
66.5, 68.1, NA, 75.1, 64.6, 69.2, 68.1, NA, 63.2, NA,
64.1, 71.5, 76.0, 69.7, 73.3, 61.7, 66.4, 65.7, 68.3, 66.9,
136.4,215.1,173.6,117.3,123.3, 96.5,178.3,191.1,158.0,193.9,
127.1,147.9,119.0,204.4,143.4,124.4,140.9,164.7,139.8,110.2,
134.1,193.6,180.0,155.0,188.2,187.4,139.2,147.9,178.6,111.1) ,
nrow=30 )
colnames(myData) <- c("height","weight")
myData <- as.data.frame(myData)
我在这里定义了一个缺失的数据索引：
# this index will help setup priors and let us look at posterior values for missing x's
mIdx <- ifelse( is.na(myData$height) , 1 , 0)
mIdx <- sapply( 1:length(mIdx),
function(n) mIdx[n]*sum(mIdx[1:n]))
# result: mIdx =
# 0, 0, 1, 2, 0, 0, 3, 0, 0, 0,
# 0, 0, 4, 0, 0, 0, 0, 5, 0, 6,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
# add missing index to myData
myData$mIdx <- mIdx
这里是数据准备部分
# DATA PREP:
y = myData[,"weight"]
x = myData[,"height"]
meanY = mean(y,na.rm=TRUE)
meanX = mean(x,na.rm=TRUE)
sdY = sd(y,na.rm=TRUE)
sdX = sd(x,na.rm=TRUE)
mIdx = myData[,"mIdx"]
Ntotal = length(y)
# Specify the data list for JAGS
dataList = list(
x = x ,
y = y ,
mIdx = mIdx ,
meanY = meanY ,
meanX = meanX ,
sdY = sdY ,
sdX = sdX ,
Ntotal = Ntotal
)
这是模特
# THE MODEL
# Standardize the data:
data {
for ( i in 1:Ntotal ) {
zx[i] <- ifelse ( mIdx[i]==0, ( x[i] - meanX ) / sdX , x[i] ) # skips NA's
zy[i] <- ( y[i] - meanY ) / sdY
}
}
# Specify the model for standardized data:
model {
for ( i in 1:Ntotal ) {
zy[i] ~ dt( zbeta0 + zbeta1 * zx[i] , 1/zsigma^2 , nu )
}
# prior for imputing missing zx's
zx ~ dnorm( 0 , 1 )
# Priors vague on standardized scale:
zbeta0 ~ dnorm( 0 , 1/(10)^2 )
zbeta1 ~ dnorm( 0 , 1/(10)^2 )
zsigma ~ dunif( 1.0E-3 , 1.0E+3 )
nu ~ dexp(1/30.0)
# Transform back to original scale:
beta1 <- zbeta1 * ysd / xsd
beta0 <- zbeta0 * ysd + ym - zbeta1 * xm * ysd / xsd
sigma <- zsigma * ysd
x <- zx*sdX + meanX
}
最后是MCMC链的初始值：
# INITIALIZE VALUES
# values hardcoded for simplicity
beta0 = 0 ; zbeta0 = 0
beta1 = 3.6 ; zbeta1 = 0.5
sigma = 30 ; zsigma = 1
nu = 30
# initial values for missing x data:
xInit = rep( NA , length(x) )
xInit[3] <- 68 ; xInit[4]<- 64 ; xInit[7] <- 68
xInit[13] <- 64 ; xInit[18] <- 68 ; xInit[20] <- 64
initsList = list( beta0=beta0 , beta1=beta1 , zbeta0=zbeta0 , zbeta1=zbeta1 ,
zsigma=zsigma , sigma=sigma, nu = nu , x=xInit )
和jags打电话：
jagsModel = jags.model( "MISSING_model.txt" , data=dataList , inits=initsList ,
n.chains=nChains , n.adapt=adaptSteps )
错误似乎是由于错误地设置了缺失数据的先验：
# imputed prior for missing zx's
zx ~ dnorm( 0 , 1 )
我读到当存在NA时，jags会自动在数据和之前的数据之间切换，但我不确定我的代码在哪里出错了zx＆＃39 >
感谢您的提示和帮助。