spvim_se

Compute standard error estimates based on the estimated influence function 
 for a SPVIM value of interest.

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

spvim_se function

<dl><dt>ics</dt>
<dd>the influence function estimates based on the contributions 
from sampling observations and sampling subsets: a list of length two 
resulting from a call to <code>spvim_ics</code>.</dd>
<dt>idx</dt>
<dd>the index of interest</dd>
<dt>gamma</dt>
<dd>the proportion of the sample size used when sampling subsets</dd>
<dt>na_rm</dt>
<dd>remove <code>NA</code>s?</dd></dl>

Arguments

Standard error estimate for SPVIM values — spvim_se

<dl>

<dt>ics</dt>
<dd>the influence function estimates based on the contributions 
from sampling observations and sampling subsets: a list of length two 
resulting from a call to <code>spvim_ics</code>.</dd>


<dt>idx</dt>
<dd>the index of interest</dd>


<dt>gamma</dt>
<dd>the proportion of the sample size used when sampling subsets</dd>


<dt>na_rm</dt>
<dd>remove <code>NA</code>s?</dd>

</dl>

spvim_se: Standard error estimate for SPVIM values

Description

Usage

Value

Arguments

Details

See Also