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RadialMR

R-CMD-check r-universe

Installation

RadialMR can be installed from the MRCIEU R-Universe with

install.packages("RadialMR", repos = c("https://mrcieu.r-universe.dev", "https://cloud.r-project.org"))

To install RadialMR directly from the GitHub repository, first make sure you have the remotes package installed:

install.packages("remotes")

Then the RadialMR package can be installed using:

remotes::install_github("WSpiller/RadialMR")

To update the package just run the command above again.

Description

We have written the RadialMR R package to produce radial plots and to perform radial regression for inverse variance weighted and MR-Egger regression models. The package contains a total of five functions:

  1. The format_radial() function is used to convert a data frame containing summary data into a set format for radial analyses.

  2. The ivw_radial() function fits a radial inverse variance weighted (IVW) model using either first order, second order, or modified second order weights. It provides an effect estimate and allows for outliers to be identified using Cochran's Q-statistic. This function now also includes iterative and exact IVW estimation, as described in: Improving the accuracy of two-sample summary data Mendelian randomization: moving beyond the NOME assumption(https://www.biorxiv.org/content/early/2018/07/02/159442).

  3. The egger_radial() function fits a radial MR-Egger model using either first order, second order, or modified second order weights. It provides an effect estimate and allows for outliers to be identified using Rucker's Q-statistic.

  4. The plotly_radial() function produces interactive radial plots corresponding to the output of the ivw_radial() and egger_radial() functions.

  5. The plot_radial() function produces a radial plot corresponding to the output of the ivw_radial() and egger_radial() functions. The function provides a range of scaling and aesthetic options showing either an IVW estimate, MR-Egger estimate, or both estimates simultaneously.

Radial plots are produced by many existing R packages such as metafor, numOSL, and Luminescence. Care will need to be taken, however, to input data from an MR-analysis appropriately into these generic platforms. For this reason we will also continue to develop our own RadialMR package to produce radial plots and conduct radial plot regression for the MR-setting.

Citation

The paper has been published in the International Journal of Epidemiology:

Bowden, J., et al., Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. International Journal of Epidemiology, 2018. 47(4): p. 1264--1278. https://doi.org/10.1093/ije/dyy101

License

This project is licensed under GNU GPL v3.