For my research I need to estimate variance, and insert these estimates into the scale matrix (called R in my JAGS-code) of my wishart distribution in JAGS. I use R2JAGS (on a mac) and I would like to know how I could do this with R/JAGS code? I want to do this automatically because I need to run a simulation study.
So I got the following R-code for running my model:
Res.NoCorr <- jags(data, model.file = "LVL_3_MODEL_SD_NoRanEff.bug", parameters =
c("sigma.mean.mu", "sigma.mean.beta", "sigma.mean.phi"), n.chains = 1, n.iter = itt,
n.burnin = bi, DIC = FALSE)
Res.NoCorr
Inference for Bugs model at "LVL_3_MODEL_SD_NoRanEff.bug", fit using jags,
1 chains, each with 1e+05 iterations (first 10000 discarded), n.thin = 90
n.sims = 1000 iterations saved
mu.vect sd.vect 2.5% 25% 50% 75% 97.5%
sigma.mean.beta 0.121 0.054 0.021 0.082 0.123 0.160 0.222
sigma.mean.mu 1.345 0.608 0.070 0.955 1.398 1.800 2.386
sigma.mean.phi 0.096 0.014 0.069 0.087 0.095 0.106 0.126
This is my JAGS code for inserting the estimates manually:
Prec.Mat[1:3, 1:3] ~ dwish(R[,], 3) # prior for precision matrix random effects
est.sd.mu <- 1.345
est.sd.beta <- 0.121
est.sd.phi <- 0.096
est.var.mu <- est.sd.mu * est.sd.mu
est.var.beta <- est.sd.beta * est.sd.beta
est.var.phi <- est.sd.phi * est.sd.phi
variance.estimation.mu <- est.var.mu * 3
variance.estimation.beta <- est.var.beta * 3
variance.estimation.phi <- est.var.phi * 3
R[1,1] <- variance.estimation.mu
R[2,2] <- variance.estimation.beta
R[3,3] <- variance.estimation.phi
R[1,2] <- 0
R[1,3] <- 0
R[2,3] <- 0
R[2,1] <- 0
R[3,1] <- 0
R[3,2] <- 0
So in this case, I need to program the insertion of the numbers of est.sd.mu, est.sd.beta and est.sd.phi.