control | R Documentation |
These functions control lower level computational details of the imputation methods.
control_bayes(
warmup = 200,
thin = 50,
chains = 1,
init = ifelse(chains > 1, "random", "mmrm"),
seed = sample.int(.Machine$integer.max, 1),
...
)
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 |
Currently only the Bayesian imputation via method_bayes()
uses a control function:
The init
argument can be set to "random"
to randomly initialise the sampler with rstan
default values or to "mmrm"
to initialise the sampler with the maximum likelihood estimate
values of the MMRM.
The seed
argument is used to set the seed for the MCMC sampler. By default, a random seed
is generated, such that outside invocation of the set.seed()
call can effectively set the
seed.
The samples are split across the chains, such that each chain produces n_samples / chains
(rounded up) samples. The total number of samples that will be returned across all chains is n_samples
as specified in method_bayes()
.
Therefore, the additional parameters passed to rstan::sampling()
must not contain
n_samples
or iter
. Instead, the number of samples must only be provided directly via the
n_samples
argument of method_bayes()
. Similarly, the refresh
argument is also not allowed
here, instead use the quiet
argument directly in draws()
.
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.
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