psi_fn

Posterior variance for emulator given points <code>n</code>.

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

psi_fn function

<dl><dt>n</dt>
<dd>Set of training set sizes to evaluate at</dd>
<dt>nset</dt>
<dd>Training set sizes for which k2() has been evaluated</dd>
<dt>var_k2</dt>
<dd>Variance of error in k2() estimate at each training set size.</dd>
<dt>N</dt>
<dd>Total number of samples on which the model will be fitted/used. Only used to rescale var_k2</dd>
<dt>var_u</dt>
<dd>Marginal variance for Gaussian process kernel. Defaults to 1e7</dd>
<dt>k_width</dt>
<dd>Kernel width for Gaussian process kernel. Defaults to 5000</dd></dl>

Arguments

Updating function for variance. — psi_fn

<dl>

<dt>n</dt>
<dd>Set of training set sizes to evaluate at</dd>


<dt>nset</dt>
<dd>Training set sizes for which k2() has been evaluated</dd>


<dt>var_k2</dt>
<dd>Variance of error in k2() estimate at each training set size.</dd>


<dt>N</dt>
<dd>Total number of samples on which the model will be fitted/used. Only used to rescale var_k2</dd>


<dt>var_u</dt>
<dd>Marginal variance for Gaussian process kernel. Defaults to 1e7</dd>


<dt>k_width</dt>
<dd>Kernel width for Gaussian process kernel. Defaults to 5000</dd>

</dl>

psi_fn: Updating function for variance.

Description

Usage

Value

Arguments

Examples