impute_data_individual {rbmi}

Impute data for a single subject

Description

This function performs the imputation for a single subject at a time implementing the process as detailed in impute().

Usage

impute_data_individual(
  id,
  index,
  beta,
  sigma,
  data,
  references,
  strategies,
  condmean,
  n_imputations = 1
)

Arguments

id

Character string identifying the subject.

index

The sample indexes which the subject belongs to e.g c(1,1,1,2,2,4).

beta

A list of beta coefficients for each sample, i.e. beta[[1]] is the set of beta coefficients for the first sample.

sigma

A list of the sigma coefficients for each sample split by group i.e. sigma[[1]][["A"]] would give the sigma coefficients for group A for the first sample.

data

A longdata object created by longDataConstructor()

references

A named vector. Identifies the references to be used when generating the imputed values. Should be of the form c("Group" = "Reference", "Group" = "Reference").

strategies

A named list of functions. Defines the imputation functions to be used. The names of the list should mirror the values specified in method column of data_ice. Default = getStrategies(). See getStrategies() for more details.

condmean

Logical. If TRUE will impute using the conditional mean values, if FALSE will impute by taking a random draw from the multivariate normal distribution.

n_imputations

When condmean = FALSE numeric representing the number of random imputations to be performed for each sample. Default is 1 (one random imputation per sample).

Details

Note that this function performs all of the required imputations for a subject at the same time. I.e. if a subject is included in samples 1,3,5,9 then all imputations (using sample-dependent imputation model parameters) are performed in one step in order to avoid having to look up a subjects's covariates and expanding them to a design matrix multiple times (which would be more computationally expensive). The function also supports subject belonging to the same sample multiple times, i.e. 1,1,2,3,5,5, as will typically occur for bootstrapped datasets.