In this vignette we describe the function bmeta for simple Bayesian meta-analysis.
## Not run:
library(jarbes)
#Example: ppvipd
data("ppvipd")
bm1 = bmeta(ppvipd)
#> module glm loaded
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 11
#> Unobserved stochastic nodes: 26
#> Total graph size: 70
#>
#> Initializing model
summary(bm1)
#> Model specifications:
#> Link function: Normal approximation
#>
#> Hyper-priors parameters:
#> Prior for mu: Normal[0, 100]
#> Prior for 1/tau^2: Scale.Gamma[0.5, 1]
#> Posterior distributions:
#> mean sd 2.5% 25% 50% 75% 97.5% Rhat
#> Mean (Pooled mean) -0.894 0.165 -1.240 -0.995 -0.886 -0.785 -0.590 1.001
#> Predictive effect -0.892 0.343 -1.642 -1.068 -0.873 -0.707 -0.206 1.001
#> Tau (between studies sd) 0.258 0.158 0.018 0.145 0.239 0.347 0.618 1.033
#> n.eff
#> Mean (Pooled mean) 12000
#> Predictive effect 9000
#> Tau (between studies sd) 210
#>
#> -------------------
#> MCMC setup (fit using jags): 2 chains, each with 10000 iterations
#> (first 1000 discarded )
#> DIC: 18.694
#> pD: 5.253
bm2 = bmeta(stemcells)
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 31
#> Unobserved stochastic nodes: 66
#> Total graph size: 167
#>
#> Initializing model
summary(bm2)
#> Model specifications:
#> Link function: Normal approximation
#>
#> Hyper-priors parameters:
#> Prior for mu: Normal[0, 100]
#> Prior for 1/tau^2: Scale.Gamma[0.5, 1]
#> Posterior distributions:
#> mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff
#> Mean (Pooled mean) 2.904 0.728 1.454 2.430 2.914 3.381 4.326 1.002 1500
#> Predictive effect 2.924 3.556 -4.073 0.616 2.908 5.248 9.911 1.001 18000
#> Tau (between studies sd) 3.426 0.611 2.407 2.993 3.365 3.795 4.774 1.001 18000
#>
#> -------------------
#> MCMC setup (fit using jags): 2 chains, each with 10000 iterations
#> (first 1000 discarded )
#> DIC: 161.087
#> pD: 30.26