export_risk_stratification_info {familiar}

Extract and export cut-off values for risk group stratification.

Description

Extract and export cut-off values for risk group stratification by models in a familiarCollection.

Usage

export_risk_stratification_info(
  object,
  dir_path = NULL,
  aggregate_results = TRUE,
  export_collection = FALSE,
  ...
)

## S4 method for signature 'familiarCollection'
export_risk_stratification_info(
  object,
  dir_path = NULL,
  aggregate_results = TRUE,
  export_collection = FALSE,
  ...
)

## S4 method for signature 'ANY'
export_risk_stratification_info(
  object,
  dir_path = NULL,
  aggregate_results = TRUE,
  export_collection = FALSE,
  ...
)

Arguments

object

A familiarCollection object, or other other objects from which a familiarCollection can be extracted. See details for more information.

dir_path

Path to folder where extracted data should be saved. NULL will allow export as a structured list of data.tables.

aggregate_results

Flag that signifies whether results should be aggregated for export.

export_collection

(optional) Exports the collection if TRUE.

...

Arguments passed on to as_familiar_collection

familiar_data_names

Names of the dataset(s). Only used if the object parameter is one or more familiarData objects.

collection_name

Name of the collection.

Details

Data is usually collected from a familiarCollection object. However, you can also provide one or more familiarData objects, that will be internally converted to a familiarCollection object. It is also possible to provide a familiarEnsemble or one or more familiarModel objects together with the data from which data is computed prior to export. Paths to the previous files can also be provided.

All parameters aside from object and dir_path are only used if object is not a familiarCollection object, or a path to one.

Stratification cut-off values are determined when creating a model, using one of several methods set by the stratification_method parameter. These values are then used to stratify samples in any new dataset. The available methods are:

• median (default): The median predicted value in the development cohort is used to stratify the samples into two risk groups.

• fixed: Samples are stratified based on the sample quantiles of the predicted values. These quantiles are defined using the stratification_threshold parameter.

• optimised: Use maximally selected rank statistics to determine the optimal threshold (Lausen and Schumacher, 1992; Hothorn et al., 2003) to stratify samples into two optimally separated risk groups.

Value

A data.table (if dir_path is not provided), or nothing, as all data is exported to csv files.

References

1. Lausen, B. & Schumacher, M. Maximally Selected Rank Statistics. Biometrics 48, 73 (1992).

2. Hothorn, T. & Lausen, B. On the exact distribution of maximally selected rank statistics. Comput. Stat. Data Anal. 43, 121–137 (2003).