# BWMR: Inference the causality based on BWMR method In jiazhao97/BWMR: Bayesian Weighted Mendelian Randomization (BWMR)

## Description

Estimate the causal effect between the exposure and the outcome.

## Usage

 `1` ```BWMR(gammahat, Gammahat, sigmaX, sigmaY) ```

## Arguments

 `gammahat` SNP-exposure effects. `Gammahat` SNP-outcome effects. `sigmaX` Standard errors of SNP-exposure effects. `sigmaY` Standard errors of SNP-outcome effects.

## Details

`BWMR` obtain the causal effect based on summary statistics.

## Value

 `mu_beta` Estimate of parameter `beta`. `se_beta` Estimate of the standard error of parameter `beta`. `P_value` P_value. `plot1` Plot of Data with Standard Error Bar. `plot2` Trace Plot of Logarithm of Approximate Data Likelihood. `plot3` Estimate of Weight of Each Data Point. `plot4` Plot of Weighted Data and Its Regression Result.

Jia Zhao

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26``` ``` library(BWMR) library(ggplot2) data(ExampleData) MRres <- BWMR(ExampleData\$beta.exposure, ExampleData\$beta.outcome, ExampleData\$se.exposure, ExampleData\$se.outcome) beta.hat <- MRres\$beta beta.se <- MRres\$se_beta P_value <- MRres\$P_value # Plot1: Plot of Data with Standard Error Bar plot1 <- MRres\$plot1 plot1 # Plot2: Trace Plot of Logarithm of Approximate Data Likelihood plot2 <- MRres\$plot2 plot2 # Plot3: Estimate of Weight of Each Data Point plot3 <- MRres\$plot3 plot3 # Plot4: Plot of Weighted Data and Its Regression Result plot4 <- MRres\$plot4 plot4 ```

jiazhao97/BWMR documentation built on Nov. 6, 2019, 1:43 p.m.