我正在尝试在 JAGS 中编写最简单的缺失数据模型。
一个预测变量（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，但它可以帮助我在继续处理更复杂的缺失预测数据场景之前解决模型问题。
模型和数据如下，但这是编译错误：
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；我确实在下面定义了它们，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
)
这是模型
# 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 )
美洲虎叫道：
jagsModel = jags.model( "MISSING_model.txt" , data=dataList , inits=initsList ,
n.chains=nChains , n.adapt=adaptSteps )
该错误似乎是由于对丢失数据的先验设置不当造成的：
# 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对于 zx 来说哪里出了问题。
感谢您的提示和帮助。
