extract_descriptives_jags: Extraction of descriptive statistics from a JAGS object

In hunansona/ed4bhm: Empirical Determinacy for Bayesian Hierarchical Models

Extraction of descriptive statistics from a JAGS object

Description

This function takes a JAGS object as input and extracts the descriptive statistics (mean and standard deviation) of all the model parameters.

Usage

extract_descriptives_jags(jags.object.base, w, prec.name)

Arguments

jags.object.base	character string, the name of the JAGS object fitted in rjags with the base model (non-weighted likelihood)
w	numeric, the weighting factor to weight the likelihood. For the base model w=1.
prec.name	character string, the name of the precision parameter to be used for logarithmic transformation of posterior samples for the precoision parameter

Value

numerical matix, the matrix has two columns with names "mean" and "sd" for the mean and standard deviation of the marginal posterior distributions and has as many rows as there are parameters in the model (without random effects)

See Also

rjags

Examples

data(eight_schools)
#prior settings
mean_mu<-0
prec_mu<-1/(4^2) 
prec_tau<-1/(5^2)

##########################################################
library(rjags)
library(coda)
#sessionInfo()
list.modules()
# In order to sample from the likelihood in JAGS one has to load the "dic" module and 
#mention "deviance" in variable.names
load.module("dic")
list.modules()

set.seed(44566)

###############
# rjags interface with code in a model string
###############

# In JAGS the normal distribution is parametrized by its mean and precision
jags_data<-list(y=eight_schools$y, sigma_2=eight_schools$prec, 
                mean_mu=mean_mu, prec_mu=prec_mu, 
                prec_tau=prec_tau)

# define parameters
schools.params.jags<-c("mu", "tau_2", "deviance")

schools_centered_modelString <- "
# likelihood
model{
for(i in 1:length(y)){
y[i] ~ dnorm(theta[i], sigma_2[i])
theta[i] ~ dnorm(mu, tau_2);
}

# priors
mu ~ dnorm(mean_mu, prec_mu);
tau ~ dnorm(0,prec_tau)T(0,);
tau_2 <- 1/(tau^2);
}
"

writeLines(schools_centered_modelString, con="TempModel.txt") # write to a file

# # model initiation
rjags.8schools  <- jags.model(
  file = "TempModel.txt",
  data = jags_data,
  n.chains = 4,
  n.adapt = 4000,
  inits = list(list(.RNG.name = "base::Wichmann-Hill", .RNG.seed = 44561),
               list(.RNG.name = "base::Wichmann-Hill", .RNG.seed = 44562),
               list(.RNG.name = "base::Wichmann-Hill", .RNG.seed = 44563),
               list(.RNG.name = "base::Wichmann-Hill", .RNG.seed = 44564)))

# burn-in
update(rjags.8schools, n.iter = 20000)
N.iter <- 2*(10^5)
n.thin <- 100
# sampling/monitoring
fit.rjags.8schools <- coda.samples(
  model = rjags.8schools ,
  variable.names = schools.params.jags,
  n.iter = N.iter,
  thin = n.thin)

del = 0.01
descriptives_jags_8schools <- extract_descriptives_jags(jags.obj = fit.rjags.8schools, 
                                                        w = 1+0.01, prec.name = "tau_2")

hunansona/ed4bhm documentation built on June 15, 2022, 6:42 p.m.