Sorry for the additional post - I received the suggestion to include the
following additional information:
*> traceback()*
5: FUN(X[[12L]], ...)
4: lapply(data, formatC, digits = digits, format = "E")
3: write.datafile(lapply(data, formatC, digits = digits, format = "E"),
file.path(dir, data.file))
2: bugs.data(data, dir = getwd(), digits)
1: bugs(data = win.data, inits = inits, parameters.to.save = params,
model.file = "PoissonGLMM.txt", n.chains = nc, n.iter = ni,
n.burn = nb, n.thin = nt, debug = TRUE, DIC = TRUE, working.directory
= getwd())
The full code I'm trying to run using R2WinBUGS is:
S <- length(levels(plot)) # Number of sites
C <- count
nobs <- length(count)
sink("PoissonGLMM.txt")
cat("
model {
# Priors
alpha ~ dnorm(0, 1.0E-4)
for(k in 1:8){
b[k] ~ dnorm(0, 1.0E-4)
}
fS[1] <- 0
for(j in 2:S){
fS[j] ~ dnorm(0, 1.0E-4)
}
# Likelihood
for(i in 1:nobs){ # loop over observations
C[i] ~ dpois(lambda[i]) # stochastic component
log(lambda[i]) <- alpha + b[1]*cday[i] + b[2]*cday2[i] + b[3]*cSoilT[i]
+
b[4]*cSoilT2[i] + b[5]*cRainAmt24[i] + b[6]*rainAmt242[i] + b[7]*soak24 +
b[8]*RHpct[i] + fS[plot[i]]
}
}
",fill=TRUE)
sink()
# Bundle data
win.data <- list(cday=cday, cday2=cday2, cSoilT=cSoilT, cSoilT2=cSoilT2,
cRainAmt24=cRainAmt24, cRainAmt242=cRainAmt242, soak24=soak24, RHpct=RHpct,
nobs=nobs, C=C, S=S, plot=plot)
# Bundle data
#win.data <- list("cday", "cday2", "cSoilT", "cSoilT2", "cRainAmt24",
"cRainAmt242", "soak24", "RHpct", "nobs", "C", "S", "plot")
# Initial Values
a.init <- rnorm(1, 0, 0.01)
b.init <- rnorm(8, 0, 0.01)
fS.init <- c(NA, rnorm(S-1, 0, 0.01))
inits <- function(){
list(alpha = a.init, b = b.init, fS = fS.init)
}
# Parameters to estimate
params <- c("alpha", "b", "fS")
# MCMC settings
nc <- 3
nb <- 300
ni <- 1000
nt <- 5
# Start Gibbs sampler
out <- bugs(data=win.data, inits=inits, parameters.to.save=params,
model.file = "PoissonGLMM.txt", n.chains=nc, n.iter=ni, n.burn=nb,
n.thin=nt, debug=TRUE, DIC=TRUE, working.directory=getwd())
print(out, dig=2)
which(out$summary[,8] > 1.1)
--
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