ipd.run {bipd} | R Documentation |
This is the core function that runs the model in our program. Before running this function, we need to specify data, prior, JAGS code, etc. using ipd.model type function.
ipd.run(
ipd,
pars.save = NULL,
inits = NULL,
n.chains = 3,
n.adapt = 1000,
n.burnin = 1000,
n.iter = 10000
)
ipd |
ipd object created from ipd.model type function |
pars.save |
parameters to save. For instance, "beta" - coefficients for main effects; "gamma" - coefficients for effect modifiers; "delta" - average treatment effect |
inits |
initial values specified for the parameters to save |
n.chains |
number of MCMC chains to sample |
n.adapt |
number of iterations for adaptation (Note that the samples from adaptation phase is non-Markovian and do not constitute a Markov chain) |
n.burnin |
number of iterations for burn-in |
n.iter |
number of iterations to run after the adaptation |
MCMC samples stored using JAGS. The returned samples have the form of mcmc.list and coda functions can be directly applied.
ds <- generate_ipdma_example(type = "continuous")
ipd <- with(ds, ipdma.model.onestage(y = y, study = studyid, treat = treat, X = cbind(z1, z2),
response = "normal", shrinkage = "none"))
samples <- ipd.run(ipd, n.chains = 3, n.burnin = 500, n.iter = 5000)