Question

我花了几天时间在openbugs上解锁错误消息但没有成功。使用R2OpenBUGS，我收到此消息：

“模型在语法上是正确的加载的数据（不在模型中的变量：xA，pA，yA，xB，pB等）变量mu.phi.alpha未定义模型必须已编译但未更新才能更改RN生成器 BugsCmds：NoCompileInits 必须在生成初始值之前编译模型必须在更新之前初始化模型必须在监视器使用之前初始化模型“ 必须在使用监视器之前初始化模型

以下是代码：

library(R2OpenBUGS)
setwd("C:\\Users\\Tiphaine\\Desktop")


mydata<-read.csv("C:\\Users\\Tiphaine\\Desktop\\TEST3.csv")

mydata

xA <- mydata$xA1
pA <- mydata$pA1
yA <- mydata$yA1
xB <- mydata$xB1
pB <- mydata$pB1
yB <- mydata$yB1
choice <-mydata$Choice

data = list("xA", "pA", "yA","xB","pB","yB")
parameters <- c("alpha", "mu.phi.alpha", "mu.alpha", "sigma.phi.alpha", "beta", "mu.phi.beta", "mu.beta", "sigma.phi.beta", "gamma", "mu.phi.gamma", "mu.gamma", "sigma.phi.gamma", "delta", "mu.phi.delta", "mu.delta", "sigma.phi.delta", "lambda", "lmu.lambda", "mu.lambda", "lsigma.lambda", "luce", "lmu.luce", "mu.luce", "lsigma.luce")
inits = function()
{

 list(mu.phi.alpha = 0.7, sigma.phi.alpha = 1, mu.phi.beta = 0.7,
      sigma.phi.beta = 1,
      mu.phi.gamma = 0.7, sigma.phi.gamma = 1, mu.phi.delta = 0.7,
      sigma.phi.delta = 1,
      lmu.lambda = 0, lsigma.lambda = 0.5, lmu.luce = 0,
      sigma.phi.luce = 0.5)

}


model.file="model5.txt"

hierarchical = bugs(data, inits, parameters,model.file, n.chains=1,     n.iter=10000, n.burnin=1000, n.thin=10,DIC=T, codaPkg=F, debug=T)

print(hierarchical)  # a rough summary
names(hierarchical)  # summarizes the variables
hierarchical$summary # more detailed summary
plot(hierarchical)   # a visual representation

以下是模型：

model {

  alpha <- phi(alpha.phi)
  luce   <- exp(lluce) 
  lambda <- exp(llambda)

  # We put group-level normal´s on the individual parameters:

  alpha.phi ~ dnorm(mu.phi.alpha,tau.phi.alpha)I(-3, 3)
  lluce   ~ dnorm(lmu.luce, ltau.luce)
  llambda ~ dnorm(lmu.lambda, ltau.lambda)

  # Here priors for the hyperdistributions are defined:
  mu.phi.alpha ~ dnorm(0,1)
  tau.phi.alpha <- pow(sigma.phi.alpha,-2)
  sigma.phi.alpha ~ dunif(0,10)

  lmu.lambda ~ dunif(-2.3, 1.61) 
  ltau.lambda  <- pow(lsigma.lambda,-2)
  lsigma.lambda ~ dunif(0,1.13)    
  lmu.luce ~ dunif(-2.3, 1.61)   
  ltau.luce  <- pow(lsigma.luce,-2)
  lsigma.luce ~ dunif(0,1.13)       

  # To obtain the mean of the hyper distribution on the wanted scale:
  mu.alpha <- phi(mu.phi.alpha) 
  mu.lambda <- exp(lmu.lambda)
  mu.luce   <- exp(lmu.luce)

  for (i in 1:10) {

    a[i]<-step(yA1[i])+lambda*(step(yA1[i])-1)
    b[i]<-step(yB1[i])+lambda*(step(yB1[i])-1)

    va[i] <- pow(xA[i],alpha)*pA[i]+ a[i]*pow(abs(yA[i]),alpha*(1-pA[i])  

    vb[i] <- pow(xB[i],alpha)*pB[i]+ b[i]*pow(abs(yB[i]),alpha)*(1-pB[i])

    C[i]<- Choice[i]-1    
    C[i] ~ dbern(binval[i])   

    binval[i] <- 1/(1+exp((vb[i]-va[i])*luce))

   }
}

Answer 1

感谢您的回复！

以下是仍然生成相同错误的简化代码和模型（即不在模型中的变量：xA，pA，yA，xB，pB等）。

library(R2OpenBUGS)
setwd("C:\\Users\\Tiphaine\\Desktop")

xA = c(100,1,100)
pA = c(0.3,0.7,0.9)
yA = c(100,1,100)
xB = c(100,1,100)
pB = c(0.3,0.7,0.9)
yB = c(100,1,100)
choice = c(1,2,1)

data = list("xA", "pA", "yA","xB","pB","yB")
parameters <- c("alpha", "mu.phi.alpha", "mu.alpha", "sigma.phi.alpha")
inits = function()
 {

 list(mu.phi.alpha = 0.7, sigma.phi.alpha = 1)

 }


model.file="model5.txt"

hierarchical = bugs(data, inits, parameters,model.file, n.chains=1,      n.iter=10000, n.burnin=1000, n.thin=10,DIC=T, codaPkg=F, debug=T)


# Some useful commands:
print(hierarchical)  # a rough summary
names(hierarchical)  # summarizes the variables
hierarchical$summary # more detailed summary
plot(hierarchical)   # a visual representation

对于模型：

model {


    # (the choice-rule parameter).

    alpha <- phi(alpha.phi)


    # We put group-level normal´s on the individual parameters:

    alpha.phi ~ dnorm(mu.phi.alpha,tau.phi.alpha)I(-3, 3)


}


# Here priors for the hyperdistributions are defined:
mu.phi.alpha ~ dnorm(0,1)
tau.phi.alpha <- pow(sigma.phi.alpha,-2)
sigma.phi.alpha ~ dunif(0,10)


# To obtain the mean of the hyper distribution on the wanted scale:
mu.alpha <- phi(mu.phi.alpha) 




        va[i] <- xA[i]*pA[i]*alpha+yA[i]*(1-pA[i])*alpha
            vb[i] <- xB[i]*pB[i]*alpha+yB[i]*(1-pB[i])*alpha


 C[i]<- choice[i]-1       
C[i] ~ dbern(binval[i])
        binval[i] <- 1/(1+exp(va[i]-vb[i]))

    }
}

错误“不在模型中的变量”使用openbugs

1 个答案: