I am running a for loop in R (as part of a power analysis for a model I ran with R2jags). At some point I want to know if my MCMC chains have converged, if not I want to skip that iteration of the loop. However, I don't want to skip to the next iteration, I want the loop start with the same iteration again. I am currently using the command 'next', but this is skipping the iteration. How do I tell my for loop to do an extra iteration? Below is the whole code, but it is basically this little bit that I am worried about:
if(up1 > 1.1 | up2 > 1.1 | up3 > 1.1 | up4 > 1.1)
next
And this is the whole code:
G.vec <- rep(NA, 1000)
#-----model------
model1 <- function(){
# likelihood
for (c in 1:16){
D.diff[c] ~ dnorm(mu, tau)
}
# priors
mu ~ dnorm(0, 10)
tau <- 1/(sd*sd)
sd ~ dunif(0, 10)
}
#---- for loop--------
for (i in 1:1000){
#--- data simulation ---------
# paired data
sim_bf <- rtnorm(16, mean = 0.4949, sd = 0.12, lower = 0.1)
sim_af <- rtnorm(16, mean = 0.3959, sd = 0.12, lower = 0.1)
diff = sim_bf - sim_af
#------setting up jags--------------
data <- list(D.diff = diff)
params <- c("mu", "tau", "sd")
inits <- function(){
list(mu = rnorm(1),
sd = rlnorm(1))
}
#-------run jags----------------
output <- jags(data=data,
# inits=inits,
parameters.to.save=params,
n.iter=1000,
n.burn=100,
n.chains=2,
n.thin=1,
model.file=model1,
progress.bar = "gui")
#-------convergence checker----------------
output.mcmc <- as.mcmc(output)
x <- gelman.diag(output.mcmc)
up1 <- x$psrf[1,2] # Approximate convergence is diagnosed when the upper limit is close to 1
up2 <- x$psrf[2,2]
up3 <- x$psrf[3,2]
up4 <- x$psrf[4,2]
if(up1 > 1.1 | up2 > 1.1 | up3 > 1.1 | up4 > 1.1)
next
# one sided t-test
lo = output$BUGSoutput$summary[2,4]
G.vec[i] <- ifelse((lo < 0), 0, 1)
}
a <- table(G.vec)
G <- a[2]/1000
G
instead of
for(i in 1:1000){...
...
if(...
next
do
i <- 0
while(i <=1000){...
i <- i+1
...
if(...
i <- i-1
break