#JAGSExamples / scripts / normal / normal_mean.R
# Basic inference.
library('rjags')
## Loading required package: coda
## Linked to JAGS 3.4.0
## Loaded modules: basemod,bugs
setwd("~/new/Bayes/Beyes/rjags/rjags1")
df <- read.csv("normal_mean.csv")
head(df)
## X
## 1 23.12064
## 2 25.55093
## 3 22.49311
## 4 29.78584
## 5 25.98852
## 6 22.53859
normal_mean_code <- '
model
{
for (i in 1:N)
{
x[i] ~ dnorm(mu, tau)
}
mu ~ dnorm(0, 0.0001)
sigma <- 3
tau <- pow(sigma, -2)
}
'
writeLines(normal_mean_code,con="normal_mean.txt")
jags <- jags.model("normal_mean.txt",
data = list('x' = with(df, X),
'N' = nrow(df)),
n.chains = 4,
n.adapt = 500)
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph Size: 108
##
## Initializing model
mcmc.samples1 <- coda.samples(jags,
c('mu'),
5000)
#png(file.path('graphs', 'normal', 'plot1.png'))
plot(mcmc.samples1)
dev.off()
## null device
## 1
summary(mcmc.samples1)
##
## Iterations = 1:5000
## Thinning interval = 1
## Number of chains = 4
## Sample size per chain = 5000
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## 25.325189 0.299984 0.002121 0.002132
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## 24.74 25.12 25.32 25.53 25.92
#normal_mean_strong_broken_prior.R
# Basic inference.
library('rjags')
setwd("~/new/Bayes/Beyes/rjags/rjags1")
df <- read.csv("normal_mean_strong_broken_prior.csv")
head(df)
## X
## 1 23.12064
## 2 25.55093
## 3 22.49311
## 4 29.78584
## 5 25.98852
## 6 22.53859
normal_mean_strong_broken_prior_code <- '
model
{
for (i in 1:N)
{
x[i] ~ dnorm(mu, tau)
}
mu ~ dunif(0, 1)
sigma <- 3
tau <- pow(sigma, -2)
}
'
writeLines(normal_mean_strong_broken_prior_code,
con="normal_mean_strong_broken_prior.txt")
jags <- jags.model("normal_mean_strong_broken_prior.txt",
data = list('x' = with(df, X),
'N' = nrow(df)),
n.chains = 4,
n.adapt = 500)
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph Size: 108
##
## Initializing model
mcmc.samples <- coda.samples(jags,
c('mu'),
1000)
#png(file.path('graphs', 'normal', 'plot4.png'))
plot(mcmc.samples)
dev.off()
## null device
## 1
summary(mcmc.samples)
##
## Iterations = 501:1500
## Thinning interval = 1
## Number of chains = 4
## Sample size per chain = 1000
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## 9.964e-01 3.726e-03 5.891e-05 1.702e-04
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## 0.9870 0.9950 0.9974 0.9989 0.9999
#normal_mean_strong_prior.R
# Basic inference.
library('rjags')
setwd("~/new/Bayes/Beyes/rjags/rjags1")
df <- read.csv("normal_mean_strong_prior.csv")
head(df)
## X
## 1 23.12064
## 2 25.55093
## 3 22.49311
## 4 29.78584
## 5 25.98852
## 6 22.53859
normal_mean_strong_prior_code <- '
model
{
for (i in 1:N)
{
x[i] ~ dnorm(mu, tau)
}
mu ~ dnorm(0, 10)
sigma <- 3
tau <- pow(sigma, -2)
}
'
writeLines(normal_mean_strong_prior_code,
con="normal_mean_strong_prior.txt")
jags <- jags.model("normal_mean_strong_prior.txt",
data = list('x' = with(df, X),
'N' = nrow(df)),
n.chains = 4,
n.adapt = 500)
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph Size: 108
##
## Initializing model
mcmc.samples <- coda.samples(jags,
c('mu'),
5000)
#png(file.path('graphs', 'normal', 'plot2.png'))
plot(mcmc.samples)
dev.off()
## null device
## 1
summary(mcmc.samples)
##
## Iterations = 1:5000
## Thinning interval = 1
## Number of chains = 4
## Sample size per chain = 5000
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## 13.328527 0.217860 0.001541 0.001525
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## 12.90 13.18 13.33 13.47 13.76
#normal_mean_strong_wrong_prior.R
# Basic inference.
library('rjags')
setwd("~/new/Bayes/Beyes/rjags/rjags1")
df <- read.csv("normal_mean_strong_wrong_prior.csv")
head(df)
## X
## 1 23.12064
## 2 25.55093
## 3 22.49311
## 4 29.78584
## 5 25.98852
## 6 22.53859
normal_mean_strong_wrong_prior_code <- '
model
{
for (i in 1:N)
{
x[i] ~ dnorm(mu, tau)
}
mu ~ dnorm(-1000, 10)
sigma <- 3
tau <- pow(sigma, -2)
}
'
writeLines(normal_mean_strong_wrong_prior_code,
con="normal_mean_strong_wrong_prior.txt")
jags <- jags.model("normal_mean_strong_wrong_prior.txt",
data = list('x' = with(df, X),
'N' = nrow(df)),
n.chains = 4,
n.adapt = 500)
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph Size: 109
##
## Initializing model
mcmc.samples <- coda.samples(jags,
c('mu'),
10000)
#png(file.path('graphs', 'normal', 'plot3.png'))
plot(mcmc.samples)
dev.off()
## null device
## 1
summary(mcmc.samples)
##
## Iterations = 1:10000
## Thinning interval = 1
## Number of chains = 4
## Sample size per chain = 10000
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## -4.604e+02 2.177e-01 1.088e-03 1.088e-03
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## -460.8 -460.5 -460.4 -460.2 -459.9