Dear R-help,
  I'm using the R2WinBUGS package and getting an error message:
 
         Error in file(file, "r") : unable to open connection
         In addition: Warning message:
         cannot open file 'codaIndex.txt', reason 'No such file or
directory' 

I'm using R 2.2.1 and WinBUGS 1.4.1 on a windows machine (XP).  My R code
and WinBUGS code is given below. The complete WinBUGS program executes
correctly in WinBUGS however I'm generating some of my inits using WinBUGS
so I may be making an error there. On the other hand, the error generated in
R seems to imply it cannot locate a file. I've checked my paths and they are
correct. Also, my data is loading correctly.
Many thanks,
Joe
 
#########################################################
 
# R code
# Runs Bayesian Ordered Logit by calling WinBUGS from R
#     ologit2.txt: WinBUGS commands
 
library(R2WinBUGS)
 
setwd("c:/docume~1/admini~1/mydocu~1/r_tuto~1")
load("oldat.Rdata") # R data file containing data frame ol.dat
                    # with vars: q02, bf23f, bf23b, bf22, bf34a, bf34.1,
bf34.2
 
   q02 <-    ol.dat$q02 
 bf23f <-  ol.dat$bf23f 
 bf23b <-  ol.dat$bf23b 
  bf22 <-   ol.dat$bf22 
 bf34a <-  ol.dat$bf34a 
bf34.1 <- ol.dat$bf34.1 
bf34.2 <- ol.dat$bf34.2 
 
N=nrow(ol.dat) 
Ncut=5  
 
data <- list("N", "q02", "bf23f", "bf23b", "bf22", "bf34a", "bf34.1",
"bf34.2", "Ncut")
 
inits <- function()
        {
           list(k=c(-5, -4, -3, -2, -1), tau=2, theta=rnorm(7, -1, 100))
        }
 
parameters <- c("k")
 
olog.out <- bugs(data, inits, parameters, 
                 model.file="c:/Documents and Settings/Administrator/My
Documents/r_tutorial/ologit2.txt",
                 n.chains = 2, n.iter = 1000,
                 bugs.directory = "c:/Program Files/WinBUGS14/")
 
########################################################
# WinBUGS code
 
model exec;  
{
 # Priors on regression coefficients
   theta[1] ~  dnorm( -1,1.0) ;   theta[2] ~  dnorm(-1,1.0)   
   theta[3] ~  dnorm(  1,1.0) ;   theta[4] ~  dnorm(-1,1.0)   
   theta[5] ~  dnorm( -1,1.0) ;   theta[6] ~  dnorm( 1,1.0)   
   theta[7] ~  dnorm( -1,1.0)                  
             
 # Priors on latent variable cutpoints  
   k[1] ~ dnorm(0, 0.1)I(    , k[2]);  k[2] ~ dnorm(0, 0.1)I(k[1], k[3])
   k[3] ~ dnorm(0, 0.1)I(k[2], k[4]);  k[4] ~ dnorm(0, 0.1)I(k[3], k[5])
   k[5] ~ dnorm(0, 0.1)I(k[4],     )
 
 # Prior on precision
   tau ~ dgamma(0.001, 0.001)
 
 # Some defs
   sigma <- sqrt(1 / tau);             log.sigma <- log(sigma);    
 
 for (i in 1 : N) 
   {
    # Prior on 
     b[i] ~ dnorm(0.0, tau)
 
    # Model Mean
     mu[i] <- theta[1] + theta[2]*bf22[i] + theta[3]*bf23b[i] +
theta[4]*bf23f[i] + theta[5]*bf34a[i] + theta[6]*bf34.1[i] +
theta[7]*bf34.2[i]
 
     for (j in 1 : Ncut) 
       { 
          # Logit Model
          # cumulative probability of lower response than j
           logit(Q[i, j]) <-  -(k[j] + mu[i] + b[i])
       }
 
   # probability that response = j
     p[i,1] <- max( min(1 - Q[i,1], 1), 0)
 
     for (j in 2 : Ncut) 
         { 
            p[i,j] <- max( min(Q[i,j-1] - Q[i,j],1), 0) 
         }
 
     p[i,(Ncut+1)] <- max( min(Q[i,Ncut], 1), 0)
     q02[i] ~ dcat(p[i, ])
   }
}

 


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