model_train

Train model using either a GLM or a random forest

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

model_train function

<dl><dt>train_data</dt>
<dd>Data to use for training; assumed to have one binary column called <code>Y</code></dd>
<dt>model_family</dt>
<dd>Either 'log_reg' for logistic regression or 'rand_forest' for random forest</dd>
<dt>...</dt>
<dd>Passed to function <code><a href="/link/glm()?package=OptHoldoutSize&version=0.1.0.1" data-mini-rdoc="OptHoldoutSize::glm()">glm()</a></code> or <code>ranger()</code></dd></dl>

Arguments

Train model (wrapper) — model_train

<dl>

<dt>train_data</dt>
<dd>Data to use for training; assumed to have one binary column called <code>Y</code></dd>


<dt>model_family</dt>
<dd>Either 'log_reg' for logistic regression or 'rand_forest' for random forest</dd>


<dt>...</dt>
<dd>Passed to function <code><a href='https://rdrr.io/r/stats/glm.html'>glm()</a></code> or <code>ranger()</code></dd>

</dl>

model_train: Train model (wrapper)

Description

Usage

Value

Arguments

Examples