get_draws_mle: 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
)

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.

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.