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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