if (requireNamespace("neojags", quietly = TRUE)){
neojags::load.neojagsmodule()
}
#> module neojags loaded
if (requireNamespace("neojags", quietly = TRUE)){
library(rjags)
}
#> Loading required package: coda
#> Linked to JAGS 4.3.2
#> Loaded modules: basemod,bugs,neojags
modelv <- jags.model(textConnection(mod), n.chains=1, inits = list(".RNG.name" = "base::Wichmann-Hill",".RNG.seed" = 314159))
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 0
#> Unobserved stochastic nodes: 100
#> Total graph size: 103
#>
#> Initializing model
model <- jags.model(textConnection(model_string), data = list(x=c(x)),n.chains=2)
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 100
#> Unobserved stochastic nodes: 4
#> Total graph size: 107
#>
#> Initializing model
summary(samples)
#>
#> Iterations = 1001:3000
#> Thinning interval = 1
#> Number of chains = 2
#> Sample size per chain = 2000
#>
#> 1. Empirical mean and standard deviation for each variable,
#> plus standard error of the mean:
#>
#> Mean SD Naive SE Time-series SE
#> mu 1.9984 0.009907 0.0001567 0.0002014
#> nu1 0.7473 0.064702 0.0010230 0.0023223
#> nu2 1.1734 0.160123 0.0025318 0.0051945
#> tau 0.9367 0.247345 0.0039109 0.0091491
#>
#> 2. Quantiles for each variable:
#>
#> 2.5% 25% 50% 75% 97.5%
#> mu 1.9790 1.9917 1.9983 2.0048 2.0182
#> nu1 0.6366 0.7010 0.7430 0.7875 0.8887
#> nu2 0.8997 1.0603 1.1567 1.2755 1.5201
#> tau 0.5284 0.7621 0.9063 1.0853 1.4941
model_string1 <- "
model {
d <- djskew.ep(0.5,2,2,2,2)
p <- pjskew.ep(0.5,2,2,2,2)
q <- qjskew.ep(0.5,2,2,2,2)
}
"
summary(samples1)
#>
#> Iterations = 1:2
#> Thinning interval = 1
#> Number of chains = 2
#> Sample size per chain = 2
#>
#> 1. Empirical mean and standard deviation for each variable,
#> plus standard error of the mean:
#>
#> Mean SD Naive SE Time-series SE
#> d 0.008864 0 0 0
#> p 0.001350 0 0 0
#> q 2.000000 0 0 0
#>
#> 2. Quantiles for each variable:
#>
#> 2.5% 25% 50% 75% 97.5%
#> d 0.008864 0.008864 0.008864 0.008864 0.008864
#> p 0.001350 0.001350 0.001350 0.001350 0.001350
#> q 2.000000 2.000000 2.000000 2.000000 2.000000