split_data

Split data into holdout and intervention sets

Simulation

Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is 'held-out' from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) <doi:10.48550/arXiv.2202.06374> (to appear in Annals of Applied Statistics) for details of methods.

James Liley

OptHoldoutSize

Estimation of Optimal Size for a Holdout Set for Updating a
Predictive Score

Sami Haidar-Wehbe

Sam Emerson

Louis Aslett

split_data function

<dl><dt>X</dt>
<dd>Matrix of observations</dd>
<dt>frac</dt>
<dd>Fraction of observations to use for the training set</dd></dl>

Arguments

Split data — split_data

<dl>

<dt>X</dt>
<dd>Matrix of observations</dd>


<dt>frac</dt>
<dd>Fraction of observations to use for the training set</dd>

</dl>

split_data: Split data

Description

Usage

Value

Arguments

Examples