print.predictiveness_measure

Prints out a table of the point estimate and standard error for a <code>predictiveness_measure</code> object.

Calculate point estimates of and valid confidence intervals for
nonparametric, algorithm-agnostic variable importance measures in high and low dimensions,
using flexible estimators of the underlying regression functions. For more information
about the methods, please see Williamson et al. (Biometrics, 2020),  Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).

Brian D. Williamson

vimp

Perform Inference on Algorithm-Agnostic Variable Importance

Jean Feng

Charlie Wolock

Noah Simon

Marco Carone

print.predictiveness_measure function

<dl><dt>x</dt>
<dd>the <code>predictiveness_measure</code> object of interest.</dd>
<dt>...</dt>
<dd>other options, see the generic <code>print</code> function.</dd></dl>

Arguments

Print predictiveness_measure objects — print.predictiveness_measure

<dl>

<dt>x</dt>
<dd>the <code>predictiveness_measure</code> object of interest.</dd>


<dt>...</dt>
<dd>other options, see the generic <code>print</code> function.</dd>

</dl>

print.predictiveness_measure: Print `predictiveness_measure` objects

Description

Usage

Arguments