get_draws_mle: Fit the base imputation model on bootstrap samples
In rbmi: Reference Based Multiple Imputation

get_draws_mle

R Documentation

Fit the base imputation model on bootstrap samples

Description

Fit the base imputation model using a ML/REML approach on a given number of bootstrap samples as specified by method$n_samples. Returns the parameter estimates from the model fit.

Usage

get_draws_mle(
  longdata,
  method,
  sample_stack,
  n_target_samples,
  first_sample_orig,
  use_samp_ids,
  failure_limit = 0,
  ncores = 1,
  quiet = FALSE
)

Arguments

`longdata`	R6 `longdata` object containing all relevant input data information.
`method`	A `method` object as generated by either `method_approxbayes()` or `method_condmean()` with argument `type = "bootstrap"`.
`sample_stack`	A stack object containing the subject ids to be used on each mmrm iteration.
`n_target_samples`	Number of samples needed to be created
`first_sample_orig`	Logical. If `TRUE` the function returns `method$n_samples + 1` samples where the first sample contains the parameter estimates from the original dataset and `method$n_samples` samples contain the parameter estimates from bootstrap samples. If `FALSE` the function returns `method$n_samples` samples containing the parameter estimates from bootstrap samples.
`use_samp_ids`	Logical. If `TRUE`, the sampled subject ids are returned. Otherwise the subject ids from the original dataset are returned. These values are used to tell `impute()` what subjects should be used to derive the imputed dataset.
`failure_limit`	Number of failed samples that are allowed before throwing an error
`ncores`	Number of processes to parallelise the job over
`quiet`	Logical, If `TRUE` will suppress printing of progress information that is printed to the console.

Details

This function takes a Stack object which contains multiple lists of patient ids. The function takes this Stack and pulls a set ids and then constructs a dataset just consisting of these patients (i.e. potentially a bootstrap or a jackknife sample).

The function then fits a MMRM model to this dataset to create a sample object. The function repeats this process until n_target_samples have been reached. If more than failure_limit samples fail to converge then the function throws an error.

After reaching the desired number of samples the function generates and returns a draws object.

Value

A draws object which is a named list containing the following:

data: R6 longdata object containing all relevant input data information.
method: A method object as generated by either method_bayes(), method_approxbayes() or method_condmean().
samples: list containing the estimated parameters of interest. Each element of samples is a named list containing the following:
- ids: vector of characters containing the ids of the subjects included in the original dataset.
- beta: numeric vector of estimated regression coefficients.
- sigma: list of estimated covariance matrices (one for each level of vars$group).
- theta: numeric vector of transformed covariances.
- failed: Logical. TRUE if the model fit failed.
- ids_samp: vector of characters containing the ids of the subjects included in the given sample.
fit: if method_bayes() is chosen, returns the MCMC Stan fit object. Otherwise NULL.
n_failures: absolute number of failures of the model fit. Relevant only for method_condmean(type = "bootstrap"), method_approxbayes() and method_bmlmi().
formula: fixed effects formula object used for the model specification.