control {rbmi}R Documentation

Control the computational details of the imputation methods

Description

These functions control lower level computational details of the imputation methods.

Usage

control_bayes(
  warmup = 200,
  thin = 50,
  chains = 1,
  init = ifelse(chains > 1, "random", "mmrm"),
  seed = sample.int(.Machine$integer.max, 1),
  ...
)

Arguments

warmup

a numeric, the number of warmup iterations for the MCMC sampler.

thin

a numeric, the thinning rate of the MCMC sampler.

chains

a numeric, the number of chains to run in parallel.

init

a character string, the method used to initialise the MCMC sampler, see the details.

seed

a numeric, the seed used to initialise the MCMC sampler.

...

additional arguments to be passed to rstan::sampling().

Details

Currently only the Bayesian imputation via method_bayes() uses a control function:

Note

For full reproducibility of the imputation results, it is required to use a set.seed() call before defining the control list, and calling the draws() function. It is not sufficient to merely set the seed argument in the control list.


[Package rbmi version 1.4.1 Index]