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SCRIPT1_CLASS.r
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SCRIPT1_CLASS.r
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rm(list=ls())
library(jagsUI)
# =========================================================================
# LMM: VIPER MASS - LENGTH FOR DIFFERENT POPULATIONS
#===========================================================================
# Does mass correlate with lenght in different visper populations?
setwd("C:/Users/Ana/Documents/PhD/Second chapter/Data/Examples")
v <- read.csv("visper.csv")
attach(v)
n.groups <- 56 # Number of populations
n.sample <- 10 # Number of vipers in each pop
n <- n.groups * n.sample
# ===================== MODEL IN FREQUENTIST =============================
library('lme4')
lme.fit1 <- lmer(mass ~ length + (1 | pop), REML = TRUE)
lme.fit1
# ===================== MODEL IN JAGS ====================================
# Specify data that will be used in the model
win.data <- list(mass = as.numeric(mass), pop = as.numeric(pop), length = length,
ngroups = max(as.numeric(pop)), n = n)
# Write model
setwd("C:/Users/Ana/Documents/PhD/Second chapter/Data/Examples")
cat("
model {
# Priors:
#RANDOM INTERCEPT PER POPULATION
for (i in 1:ngroups){
alpha[i] ~ dnorm(mu.int, tau.int) # Random intercepts
}
mu.int ~ dnorm(0, 0.001) # Mean hyperparameter for random intercepts
tau.int <- 1 / (sigma.int * sigma.int)
sigma.int ~ dunif(0, 100) # SD hyperparameter for random intercepts
#COMMON SLOPE FOR ALL POPULATIONS
beta ~ dnorm(0, 0.001) # Common slope
tau <- 1 / ( sigma * sigma) # Residual precision
sigma ~ dunif(0, 100) # Residual standard deviation
# Likelihood
for (i in 1:n) {
mass[i] ~ dnorm(mu[i], tau) # The random variable
mu[i] <- alpha[pop[i]] + beta* length[i] # Expectation
}
}
",fill = TRUE, file = "lmm.txt")
# Inits function
inits <- function(){list(alpha = rnorm(n.groups, 0, 2), beta = rnorm(1, 1, 1),
mu.int = rnorm(1, 0, 1), sigma.int = rlnorm(1), sigma = rlnorm(1))}
# Parameters to estimate
parameters <- c("alpha", "beta", "mu.int", "sigma.int", "sigma")
# MCMC settings
ni <- 2000
nb <- 500
nt <- 2
nc <- 3
# Run the model in JAGS
out <- jags(win.data, inits, parameters, "lmm.txt", n.chains = nc,
n.thin = nt, n.iter = ni, n.burnin = nb, parallel = TRUE)
# Inspect results
print(out, dig = 3)
traceplot(out)
traceplot(out, param = c("mu.int", "sigma.int", "beta"))
print(out, 2)
# Plot
par(mfrow = c(1,2))
plot(density(out$sims.list$mu.int), xlab="alpha.mean", ylab="Frequency", frame = F)
abline(v = 230, col = "red", lwd = 3)
# Compare with input values??????
intercept.mean ; slope.mean ; intercept.sd ; slope.sd ; sd(eps)