Я пытаюсь использовать R2WinBUGS, используя этот пример:
код
(Пожалуйста, рассмотрите только часть: ### 5.4. Анализ с использованием WinBUGS)
Я получаю это сообщение об ошибке:
Error in file(con, "wb") : cannot open the connection
In addition: Warning messages:
1: In file.create(to[okay]) :
cannot create file 'c:/Program Files/WinBUGS14//System/Rsrc/Registry_Rsave.odc', reason 'Permission denied'
2: In file(con, "wb") :
cannot open file 'c:/Program Files/WinBUGS14//System/Rsrc/Registry.odc': Permission denied
Warning message:
running command '"c:/Program Files/WinBUGS14//WinBUGS14.exe" /par "D:/R2WinBUGS/normal/script.txt"' had status 1
>
Я не уверен, имеет ли это решающее значение для правильной работы (все остальное выглядит нормально). Есть ли способ избавиться от этого?
Спасибо.
Christian
PS:
Это код R:
library(R2WinBUGS)
setwd("D:/R2WinBUGS/normal")
y10 <- rnorm(n = 10, mean = 600, sd = 30) # Sample of 10 birds
y1000 <- rnorm(n = 1000, mean = 600, sd = 30) # Sample of 1000 birds
# Save BUGS description of the model to working directory
sink("model.txt")
cat("
model {
# Priors
population.mean ~ dunif(0,5000) # Normal parameterized by precision
precision <- 1 / population.variance # Precision = 1/variance
population.variance <- population.sd * population.sd
population.sd ~ dunif(0,100)
# Likelihood
for(i in 1:nobs){
mass[i] ~ dnorm(population.mean, precision)
}
}
",fill=TRUE)
sink()
# Package all the stuff to be handed over to WinBUGS
# Bundle data
win.data <- list(mass = y1000, nobs = length(y1000))
# Function to generate starting values
inits <- function()
list (population.mean = rnorm(1,600), population.sd = runif(1, 1, 30))
# Parameters to be monitored (= to estimate)
params <- c("population.mean", "population.sd", "population.variance")
# MCMC settings
nc <- 3 # Number of chains
ni <- 1000 # Number of draws from posterior (for each chain)
nb <- 1 # Number of draws to discard as burn-in
nt <- 1 # Thinning rate
# Start Gibbs sampler: Run model in WinBUGS and save results in object called out
out <- bugs(data = win.data, inits = inits, parameters.to.save = params, model.file = "model.txt",
n.thin = nt, n.chains = nc, n.burnin = nb, n.iter = ni, debug = TRUE, DIC = TRUE, working.directory = getwd())
ls()
out # Produces a summary of the object
names(out)
str(out)
hist(out$summary[,8]) # Rhat values in the eighth column of the summary
which(out$summary[,8] > 1.1) # None in this case
par(mfrow = c(3,1))
matplot(out$sims.array[1:999,1:3,1], type = "l")
matplot(out$sims.array[,,2] , type = "l")
matplot(out$sims.array[,,3] , type = "l")
par(mfrow = c(3,1))
matplot(out$sims.array[1:20,1:3,1], type = "l")
matplot(out$sims.array[1:20,,2] , type = "l")
matplot(out$sims.array[1:20,,3] , type = "l")
par(mfrow = c(3,1))
hist(out$sims.list$population.mean, col = "grey")
hist(out$sims.list$population.sd, col = "blue")
hist(out$sims.list$population.variance, col = "green")
par(mfrow = c(1,1))
plot(out$sims.list$population.mean, out$sims.list$population.sd)
pairs(cbind(out$sims.list$population.mean, out$sims.list$population.sd, out$sims.list$population.variance))
summary(out$sims.list$population.mean)
summary(out$sims.list$population.sd)
sd(out$sims.list$population.mean)
sd(out$sims.list$population.sd)
summary(lm(y1000 ~ 1))