## Analysis of MCMC output using the coda package
library(coda)
data(obsBea)
## Different starting values
resB1 <- MCMCc4photo(obsBea, 2e5, scale=1.5)
resB2 <- MCMCc4photo(obsBea, 2e5, ivmax=25, ialpha=0.1, scale=1.5)
resB3 <- MCMCc4photo(obsBea, 2e5, ivmax=45, ialpha=0.02, scale=1.5)
## Creating the mcmc objects
mc1 <- mcmc(resB1$resuMC[,1:2])
mc2 <- mcmc(resB2$resuMC[,1:2])
mc3 <- mcmc(resB3$resuMC[,1:2])
mcl <- mcmc.list(mc1,mc2,mc3)
acfplot(mcl)
## I apply a burn in and thinning
mc1 <- mcmc(resB1$resuMC[1e3:2e5,1:2], thin = 100)
mc2 <- mcmc(resB2$resuMC[1e3:2e5,1:2], thin = 100)
mc3 <- mcmc(resB3$resuMC[1e3:2e5,1:2], thin = 100)
mcl <- mcmc.list(mc1,mc2,mc3)
gelman.diag(mcl)
plot(mcl)
xyplot(mcl)
autocorr.diag(mcl)
summary(mcl)
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