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RsqMed (version 1.1)

Total Mediation Effect Size Measure for High-Dimensional Mediators

Description

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" . 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" .

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Version

Install

install.packages('RsqMed')

Monthly Downloads

161

Version

1.1

License

GPL-3

Maintainer

Tianzhong Yang

Last Published

November 14th, 2023

Functions in RsqMed (1.1)

CF_Rsq.measure

Function to calculate the Rsq function as a total effect size measure for mediation effect using cross-fitted estimation
Rsq.measure

Function to calculate the Rsq function as a total mediation effect size measure (Gaussian outcome only). If method = 'iSIS', a two-step procedure is performed, where the first step filters the non-mediators based on the first p proportion of the data and the second step calculates the point estimates for Rsq using random-effect models on the remaining data. If method = 'ALL', Rsq is calculated based on all subjects and mediators (assuming all mediators are the true mediators). It is optional to adding filtering step on putative mediators to exclude M1 type of non-mediators (See Yang et al, BMC bioinformatics, 2021).
example

Example dataset
CI.Rsq.measure

Functions to generate the confidence interval of the Rsq measure using nonparametric bootstrap.