Run Jags - extract multiple realisations from mcmc object

Question

I have a runjags script that generates predicted burrow density for every cell on an island. I'm looking to obtain multiple draws (around 100) from an mcmc object for every cell. My dissertation supervisor thinks I should be able to do this using the coda package but I've only been able to extract the mean value for each cell rather than multiple realisations.

Code used to run the model and extract the mean values:

runjags.options(force.summary=TRUE)
print(runjags.options())
S2VS1_best_fit_result <- run.jags(model=S2VS1_best_fit_model, burnin=100000, sample=1000, n.chains=3, modules="glm", thin = 100)
S2_result <- as.mcmc(S2VS1_best_fit_result, vars = "S2")
S2_result_list <- as.mcmc.list(S2VS1_best_fit_result, vars = "S2")
S1_summary <- summary(S2_result_list)
S1_stats <- S2_summary$statistics

Can anyone tell me how to get multiple values for each cell?

The model:

S2VS1_best_fit_model <- "model{
for(i in 1:K) { # Cells loop
S2[i]~dpois(lambda1[i])
lambda1[i]<- exp(a0+a1*normalise_DEM_aspect[i]+a2*normalise_DEM_elevation[i]+a3*normalise_DEM_slope[i]+
a4*normalise_DEM_elevation[i]*normalise_DEM_slope[i]+
a5*normalise_sentinel5[i]+a6*normalise_sentinel10[i]+
a8*S1[i]+
a9*Tussac[i])

muLogit_tussac[i]<-b0+b1*normalise_sentinel1[i]+b2*normalise_sentinel7[i]+b3*normalise_sentinel8[i]+
               b4*normalise_sentinel9[i]+b5*normalise_DEM_slope[i]

Logit_tussac[i]~dnorm(muLogit_tussac[i], tau) # tau = precision (1/variance or 1/sd^2) - see Lecture 5, Slide 17
Tussac[i]<-exp(Logit_tussac[i])/(1+exp(Logit_tussac[i]))

S1[i]~dpois(lambda2[i])
lambda2[i]<-exp(c0)

}

# Priors

a0~dnorm(0, 10)
a1~dnorm(0, 10)
a2~dnorm(0, 10)
a3~dnorm(0, 10)
a4~dnorm(0, 10)
a5~dnorm(0, 10)
a6~dnorm(0, 10)
a7~dnorm(0, 10)
a8~dnorm(0, 10)
a9~dnorm(0, 10)

b0~dnorm(0, 10)
b1~dnorm(0, 10)
b2~dnorm(0, 10)
b3~dnorm(0, 10)
b4~dnorm(0, 10)
b5~dnorm(0, 10)

c0~dnorm(0, 10)

tau~dgamma(0.001, 0.001)

#data# S1, S2, K
#data# normalise_sentinel1, normalise_sentinel5, normalise_sentinel7
#data# normalise_sentinel9, normalise_sentinel8, normalise_sentinel10
#data# normalise_DEM_aspect, normalise_DEM_elevation, normalise_DEM_slope
#inits# a0, a1, a2, a3, a4, a5
#inits# b0, b1, b2, b3, b4, b5
#inits# c0
#monitor# a0, a1, a2, a3, a4, a5, b0
#monitor# b0, b1, b2, b3, b4, b5
#monitor# c0
#monitor# ped, dic
#monitor# S1, S2
}"

Top 5 rows of dataset:

S1 S2 Logit_tussac moisture DEM_slope DEM_aspect DEM_elevation sentinel1 sentinel2 sentinel3 sentinel4 sentinel5 sentinel6 sentinel7 sentinel8 sentinel9 sentinel10
NA NA        NA        NA   14.917334   256.1612      12.24432    0.0513    0.0588    0.0541    0.1145    0.1676    0.1988    0.1977    0.1658    0.1566     0.0770
0  0  -9.210240         1   23.803741   225.1231      16.88028    0.1058    0.1370    0.2139    0.2387    0.2654    0.2933    0.3235    0.2928    0.3093     0.1601
NA NA        NA        NA   20.789165   306.0945      18.52480    0.0287    0.0279    0.0271    0.0276    0.0290    0.0321    0.0346    0.0452    0.0475     0.0219
NA NA -9.210240         1    6.689442   287.9641      36.08975    0.0462    0.0679    0.1274    0.1535    0.1797    0.2201    0.2982    0.2545    0.4170     0.2252
0  0  -9.210240         1   25.476444   203.0659      23.59964    0.0758    0.1041    0.1326    0.1571    0.2143    0.2486    0.2939    0.2536    0.3336     0.1937
1  0  -1.385919         3    1.672511   270.0000      39.55215    0.0466    0.0716    0.1227    0.1482    0.2215    0.2715    0.3334    0.2903    0.3577     0.1957

Thanks in advance for any responses.

Answer 1

Yes, you can do this by simply extracting the MCMC object from within the runjags object. Example model:

X <- 1:100
Y <- rnorm(length(X), 2*X + 10, 1)

model <- "model { 
for(i in 1 : N){ 
    Y[i] ~ dnorm(true.y[i], precision);
    true.y[i] <- (m * X[i]) + c
} 
m ~ dunif(-1000,1000)
c ~ dunif(-1000,1000) 
precision ~ dexp(1)
}"

data <- list(X=X, Y=Y, N=length(X))

results <- run.jags(model=model, monitor=c("m", "c", "precision"), 
data=data, n.chains=2)

From which we can obtain summary statistics as a matrix:

summary(results)

Or a given number of iterations from the posterior as an MCMC matrix:

combine.mcmc(results, return.samples=10)

In this case we ask for 10 iterations, and the combine.mcmc function ensures that they are evenly spaced from the posterior in order to minimise effects of autocorrelation within the chains.

Or to use the tools in the coda package to do the same thing:

allmcmc <- coda::as.mcmc(results)
window(allmcmc, thin=1000)

Matt