error "variables not in the model" using openbugs

Question

I have spent several days to unlock an error message on openbugs without success. Using R2OpenBUGS, I get this message:

"model is syntactically correct data loaded (variables not in the model: xA, pA, yA, xB, pB and others) variable mu.phi.alpha is not defined model must have been compiled but not updated to be able to change RN generator BugsCmds:NoCompileInits model must be compiled before generating initial values model must be initialized before updating model must be initialized before monitors used" model must be initialized before monitors used

Here is the code:

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

Here is the model:

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))

   }
}

It would be useful if you can make your code reproducible by providing a minimal amount of data to reproduce your error codes — guyabel

T S T S · Accepted Answer · 2015-04-15T02:04:36

thanks for your reply!

Below is a simplified code and model which still generate the same error (ie variables not in the model: xA, pA, yA, xB, pB and others).

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

And for the model:

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]))

    }
}

error "variables not in the model" using openbugs

1 Answers