Analyse Multiple Imputed Datasets

This function takes multiple imputed datasets (as generated by the impute() function from the rbmi package) and runs an analysis function on each of them.

Use rbmi::analyse() instead.

Usage

rbmi_analyse(
  imputations,
  fun = rbmi_ancova,
  delta = NULL,
  ...,
  cluster_or_cores = 1,
  .validate = TRUE
)

Arguments

imputations

An imputations object as created by the impute() function from the rbmi package.

fun

An analysis function to be applied to each imputed dataset. See details.

delta

A data.frame containing the delta transformation to be applied to the imputed datasets prior to running fun. See details.

...

Additional arguments passed onto fun.

cluster_or_cores

(numeric or cluster object)
The number of parallel processes to use when running this function. Can also be a cluster object created by make_rbmi_cluster(). See the parallelisation section below.

.validate

(logical)
Should imputations be checked to ensure it conforms to the required format (default = TRUE) ? Can gain a small performance increase if this is set to FALSE when analysing a large number of samples.

Value

An analysis object, as defined by rbmi, representing the desired analysis applied to each of the imputed datasets in imputations.

Details

This function works by performing the following steps:

1. Extract a dataset from the imputations object.

2. Apply any delta adjustments as specified by the delta argument.

3. Run the analysis function fun on the dataset.

4. Repeat steps 1-3 across all of the datasets inside the imputations object.

5. Collect and return all of the analysis results.

The analysis function fun must take a data.frame as its first argument. All other options to rbmi_analyse() are passed onto fun