add_mcmc: Generating posterior samples from MCMC
In psborrow: Bayesian Dynamic Borrowing with Propensity Score
add_mcmc R Documentation
Generating posterior samples from MCMC
Description
Generating posterior samples from MCMC
Usage
add_mcmc(dt, priorObj, n.chains, n.adapt, n.burn, n.iter, seed)
Arguments
dt
a list of matrix containing simulated time-to-events information
priorObj
an object of class .priorClass generated in set_prior
n.chains
number of parallel chains for the model
n.adapt
number of iterations for adaptation
n.burn
number of iterations discarded as burn-in
n.iter
number of iterations to monitor
seed
the seed of random number generator. Default is the first element of .Random.seed
Value
A list containing hazard ratio and prior information
psborrow documentation built on March 7, 2023, 8:32 p.m.
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| add_mcmc | R Documentation |
Generating posterior samples from MCMC
Description
Generating posterior samples from MCMC
Usage
add_mcmc(dt, priorObj, n.chains, n.adapt, n.burn, n.iter, seed)
Arguments
dt |
a list of |
priorObj |
an object of class |
n.chains |
number of parallel chains for the model |
n.adapt |
number of iterations for adaptation |
n.burn |
number of iterations discarded as burn-in |
n.iter |
number of iterations to monitor |
seed |
the seed of random number generator. Default is the first element of .Random.seed |
Value
A list containing hazard ratio and prior information
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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