example

An implementation of calculating the R-squared measure as a total mediation effect size measure and its confidence interval for moderate- or high-dimensional mediator models. It gives an option to filter out non-mediators using variable selection methods. The original R package is directly related to the paper Yang et al (2021) "Estimation of mediation effect for high-dimensional omics mediators with application to the Framingham Heart Study" <doi:10.1101/774877>. The new version contains a choice of using cross-fitting, which is computationally faster. The details of the cross-fitting method are available in the paper Xu et al (2023) "Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting" <doi:10.1101/2023.02.06.527391>.

Tianzhong Yang

RsqMed

Total Mediation Effect Size Measure for High-Dimensional
Mediators

Chunlin Li

Zhichao Xu

example function

Contains the following objects:<dl>
 <dt>Y</dt>
<dd>a vector of 100 observations, representing outcome.</dd> <dt>X</dt>
<dd>a vector of 100 observations, representing independent variable.</dd> <dt>M</dt>
<dd>a 100 * 100 matrix, representing mediators.</dd> <dt>Cov</dt>
<dd>a 100 * 2 matrix, representing covariates.</dd> 
</dl>

Format

Example dataset — example

Contains the following objects:<dl>
 <dt>Y</dt>
<dd>a vector of 100 observations, representing outcome.</dd>

 <dt>X</dt>
<dd>a vector of 100 observations, representing independent variable.</dd>

 <dt>M</dt>
<dd>a 100 * 100 matrix, representing mediators.</dd>

 <dt>Cov</dt>
<dd>a 100 * 2 matrix, representing covariates.</dd>

 
</dl>

example: Example dataset

Description

Arguments

Format