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# Untitled

a guest Feb 13th, 2018 63 Never
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1. # Generation of simulated data
2. set.seed(123)
3. varY <- rnorm(100, 0, 1)
4. facX <- gl(n = 4, k = 25, labels = c("A", "B", "C", "D"))
5. block <- factor(rep(x = paste("B",1:25, sep = ""), times = 4))
6. df <- data.frame(varY, facX, block)
7.
8. # Frequentist analysis
9. library(lme4)
10. model.freq <- lmer(varY ~ facX + (1|block), data = df)
11.
12. # Data reshaping
13. matY <- matrix(data = varY, nrow = 25, ncol = 4, byrow = FALSE)
14.
15. # Model specification
16. model.bayes <- function(){
17. # Likelihood
18.  for(j in 1:Nlev){
19.   for(i in 1:N){
20.    varY[i,j] ~ dnorm(mu + theta[j], 1/(sig*sig))
21.    }
22.  }
23. theta[1] <- 0
24.   for(j in 2:Nlev){
25.    theta[j] ~ dnorm(0, 0.001)
26.   }
27. # Priors
28. mu ~ dnorm(0, 0.001)
29. sig ~ dunif(0, 1000)
30. }
31.
32. # Bayesian analysis with JAGS
33. dat <- list(varY = matY, Nlev = ncol(matY), N = nrow(matY))
34. params <- c("mu", "theta", "sig")
35. inits <- function(){list(mu = rnorm(1), sig = rlnorm(1))}
36.
37. library(R2jags)
38. out <- jags(data = dat, inits = inits, parameters.to.save = params, model.file = model.bayes, n.chains = 3, n.iter = 10000, n.burnin = 1000, n.thin = 1)
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