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MR_Practicical_Session_MB_2023.Rmd
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MR_Practicical_Session_MB_2023.Rmd
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---
title: "Mendelian Randomization course\nPractical Session"
author: "Mathilde Boissel"
date: "27/10/2023"
output:
html_document:
toc: true
toc_depth: 1
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
_It is highly recommanded to follow the theorical session before start this training._
The aim of this session will be explore several methods/packages to eval estimates usually wanted in MR studies.
---
# R environment
In the following session, under R 4.2.1, we will use following R packages :
- [{ivpack}](https://cran.r-project.org/web/packages/ivpack/index.html) v1.2
- [{meta}](https://cran.r-project.org/web/packages/meta/index.html) v6.1-0 (ou 6.2-1 aussi testé)
- [{MendelianRandomization}](https://cran.r-project.org/web/packages/MendelianRandomization/index.html) v0.7.0
- [{TwoSampleMR}](https://mrcieu.github.io/TwoSampleMR/articles/introduction.html) v0.5.6 (ou 0.5.7 aussi testé)
**Warning** : Loading `{TwoSampleMR}` leads to conflict with `{MendelianRandomization}` masking `mr_ivw` and `mr_median` functions.
It is also worth noting the function `dat_to_MRInput` in the `{TwoSampleMR}` package that will convert from the `TwoSampleMR` format to the `MendelianRandomization` format.
```{r packages, echo = TRUE, include = FALSE}
# install.packages(c("ivpack", "meta", "MendelianRandomization", "devtools"))
# # or with renv
# library(renv)
# renv::install("testthat@3.1.6")
# renv::install("ivpack@1.2")
# renv::install("Rcpp")
# renv::install("meta")
# renv::update("htmltools")
# renv::install("MendelianRandomization@0.7.0")
# renv::install("MRCIEU/TwoSampleMR")
# # or via devtools
# library(devtools) ## devtools only need to install from github
# install_github('MRCIEU/TwoSampleMR')
library(ivpack)
library(meta)
library(MendelianRandomization)
# library(TwoSampleMR) # to load later otherwise conflict :
# The following objects are masked from 'package:MendelianRandomization':
# mr_ivw, mr_median
```
---
# Dataset
You have to open the Rmd file `MR_Practical_Session.Rmd` to access to not-shown-chunk named `dataset`, so you will get the data by copy and past the objects (`coursedata`, `ratio.all` and `diabetes_data`) in your R Console.
**Warning**: the dataset we gave you was already **harmonised** - all the associations were matched up to be on the **same strand**, and **going in the same direction**. In reality, you will often need to check that these **alignments** are correct before using the data.
MR-Base web-app can do this for you automatically under certain set rules (e.g. **remove all palindromic snps with a MAF>=0.3**), but we highly highly recommend doing this by hand if you are unfamiliar with the process and the choices being made.
```{r dataset, echo = FALSE, eval = TRUE}
#### source data copied in an R script ####
source("load_data_MR.R")
## or copy the build obj bellow :
#### coursedata 1000 lignes ####
coursedata <- structure(
list(
ID = 1:1000,
g1 = c(
1L, 0L, 1L, 0L, 0L, 2L, 1L,
1L, 1L, 2L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 2L, 0L, 0L, 0L, 2L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 2L, 2L, 1L,
0L, 2L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 1L, 0L,
0L, 1L, 0L, 0L, 1L, 1L, 2L, 1L, 2L, 0L, 0L, 0L, 0L, 0L, 2L, 1L,
1L, 1L, 0L, 0L, 2L, 1L, 0L, 0L, 0L, 1L, 1L, 2L, 1L, 0L, 0L, 1L,
0L, 1L, 0L, 2L, 1L, 1L, 0L, 2L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 0L, 1L, 0L, 1L, 2L, 0L, 1L, 0L, 1L, 2L, 1L, 0L, 0L, 1L,
1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 2L, 1L, 2L, 0L,
1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L,
1L, 0L, 1L, 1L, 0L, 2L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 0L, 2L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 2L, 0L, 2L, 1L, 2L, 0L,
0L, 0L, 1L, 1L, 1L, 2L, 0L, 0L, 2L, 0L, 2L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 2L, 1L, 0L, 0L, 0L, 1L, 2L, 2L, 2L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 2L, 0L,
1L, 2L, 2L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L,
0L, 1L, 0L, 1L, 0L, 2L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 2L,
0L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 0L, 2L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 2L, 1L,
1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
2L, 1L, 2L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L,
2L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 2L, 1L, 1L, 1L, 0L,
1L, 2L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L,
1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 2L, 0L, 1L, 0L, 1L,
1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L,
0L, 0L, 1L, 0L, 0L, 1L, 0L, 2L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 0L, 1L, 2L, 0L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 1L, 0L, 0L, 1L, 2L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L,
1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L,
2L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 2L, 1L, 0L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 2L, 1L, 0L, 0L,
0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 2L, 0L, 0L, 1L, 0L, 1L,
1L, 2L, 2L, 0L, 2L, 0L, 0L, 1L, 2L, 0L, 0L, 1L, 1L, 1L, 0L, 0L,
1L, 0L, 1L, 0L, 1L, 1L, 2L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 2L, 0L,
1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 2L, 0L, 0L, 1L,
0L, 2L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 2L, 1L, 0L, 0L, 0L,
0L, 1L, 1L, 2L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L,
1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L,
1L, 0L, 2L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 2L, 0L, 0L, 1L, 2L,
0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 2L, 1L, 1L, 1L,
0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L,
2L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 2L, 1L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 1L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 2L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 2L, 0L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 2L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 2L, 0L, 1L,
2L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 2L, 1L,
0L, 2L, 0L, 0L, 1L, 0L, 2L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L,
0L, 1L, 2L, 2L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 2L, 0L, 1L, 0L, 2L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L,
0L, 0L, 2L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 2L, 0L, 1L, 1L, 1L, 0L,
1L
),
g2 = c(
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 2L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L,
2L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L,
1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L,
0L, 0L, 1L, 0L, 0L, 1L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 2L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 2L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 2L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 2L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L,
0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L
),
g3 = c(
0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 2L,
0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L,
1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 2L,
0L, 2L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L,
0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
2L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
2L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 2L, 0L,
1L, 1L, 0L, 0L, 0L
),
g4 = c(
1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
2L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 2L, 0L, 0L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 2L, 1L, 0L, 1L, 0L, 2L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 2L, 0L, 0L, 1L, 0L,
1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 2L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 2L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L,
1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 2L, 0L, 0L,
1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L,
2L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L
),
x = c(
0.511414, -1.054505, -0.5629682, 0.8614941, 0.01561589,
0.6833003, -0.6746297, -0.4061614, -0.6149677, -1.879157,
-0.05042828, 1.680699, 0.5137487, 2.201788, 0.7167764, -1.004449,
-0.6859939, -0.6415381, 0.1375836, -0.8205097, -0.9211929, -0.2890304,
2.804525, 1.226967, -0.9575722, -1.270487, -2.289114, 0.526536, 1.438994,
-0.3715216, 1.331559, 1.792797, 1.692925, 1.256187, 0.06991528, -0.6505202,
1.396369, -0.5114426, 0.2672284, 1.574073, 0.04566857, 0.4056919, 1.818845,
-2.555156, 2.999357, 1.600707, 2.371696, -0.7393405, -0.2391566, -1.39762,
-1.739929, 0.7721316, 1.411083, -1.757184, 0.5943157, -0.2505998, -0.1681945,
0.2570663, 3.30867, -1.116718, 0.5894963, 1.478647, -0.7417048, -1.319887,
1.584719, 1.15919, -0.8447209, 0.2667319, 1.18326, 0.02816468, 1.687759,
-1.199569, -0.6692825, 0.5739241, 2.120731, 0.8224431, 0.6210457, -2.017137,
-0.6241882, 2.268948, 1.805351, 0.397793, -0.7359586, -0.2063584, 0.1522673,
1.036336, 1.330475, 0.2612113, -0.9154014, -1.5377, -1.098441, 3.086792,
0.6043353, 1.170345, -3.111301, 0.2640477, 2.536658, -0.2510893, 1.401259,
0.1661447, 1.686695, -0.1609624, 0.1630675, 0.415674, -1.550273, 0.1489423,
-0.1576676, -0.5691448, -0.6539119, 0.4012272, -0.8691397, 0.7204053,
-0.7049306, -0.1225196, -1.265644, -0.8104727, 0.5801718, 0.2097785,
0.287809, -0.4992997, 1.801467, -2.634428, 0.8473001, -0.6796415,
-0.7637564, -1.097179, 0.4107748, 0.1740537, 1.315861, 0.6626521,
0.1578388, 3.80543, 0.7432005, -0.3357377, 1.604536, -1.176574, 2.478271,
-0.2291014, 0.4579258, 3.248699, 2.709125, 2.162149, -0.0405159, 0.9048444,
-1.348854, 0.8823482, 0.1021243, -3.259865, -1.067268, 0.2819568,
-0.6596893, -0.1807872, 2.515758, -0.17833, 1.469117, -0.7612572,
1.067393, 0.7137753, 1.316876, 0.8889861, -0.1705705, 0.930299,
2.178425, 1.040393, 0.2484525, 1.6969, -0.2385769, 1.215803,
-0.121749, 0.4517173, 2.711227, -1.034289, 1.360948, 0.3336845,
1.448049, 3.336718, -0.5653164, 1.116664, 1.71992, 2.103569, -0.676021,
0.734796, 1.041596, 1.132922, -1.65245, 0.01402473, 0.0465818, -1.975893,
1.67299, 2.275564, -0.2249914, 0.7790609, 3.159228, 1.638781, 1.436377,
-0.6704336, 1.45016, -0.6534693, 3.628212, 0.683499, -1.210783, 1.252596,
2.002806, 0.9677379, 1.181196, 1.100068, -2.424559, 1.123638, -1.586164,
1.083465, 0.0608369, 2.765038, 0.8396216, 1.458574, 0.1989703, 1.006285,
2.162564, -0.603887, -2.31755, 1.221963, 0.6493447, 2.953362, 0.5976394,
-0.3981365, -1.78078, -0.496523, 0.860883, -1.298076, -2.297513,
-0.09740282, 2.072371, -1.799149, 0.2757767, -0.5535889, 0.1776869,
0.1154027, -1.494918, -1.597056, -3.715302, 1.396965, 1.987818, 0.6799783,
-0.8720844, 2.98124, 1.782676, 2.107667, -0.7132319, -0.3419962,
-1.702036, -0.1761597, 3.431252, 3.155451, 0.9332177, 1.979404, -1.7355,
0.6880204, 1.051234, 1.89421, 0.4698883, -0.3932978, 0.3974729,
-0.9357116, -0.2343248, 0.2050391, -0.3535254, 0.4131892, -3.710254,
0.2160358, -1.364176, 1.031962, -0.3941657, 0.06128966, -0.9381835,
0.2668205, -0.04478533, 0.5474066, 0.8188285, 1.672575, 1.827346,
-1.142377, 0.1020187, -0.1820338, -1.706108, -2.439444, 1.259864,
-0.818278, 1.996407, -2.080284, 0.8619137, 0.8588534, 1.868601, 1.256401,
-2.316555, 0.3826919, -1.012581, 2.328252, 0.5380796, 0.7670646,
-0.05206852, -0.6185147, -1.53058, 0.7413501, 0.4799917, 1.671116,
1.326772, 0.261943, 1.871066, -1.371915, 1.57526, -1.715803, 2.569946,
2.241288, -0.8802445, -1.4776, 0.6931523, 0.8917982, 0.5523826, 1.245605,
1.833399, 2.667176, 1.468033, 1.628667, 0.8040985, -0.3787018, -0.4190084,
-0.6459137, -0.5960377, 0.2484855, -0.188875, 1.130984, -0.3331705,
-0.5551823, -1.085202, 1.318145, -0.7794812, -1.406285, 0.3070447,
-1.347552, 1.327245, 1.353192, -0.4580307, 1.521133, 0.6713348, 2.54509,
0.8640877, 1.292991, -0.7605298, -2.607158, 0.1165786, 1.868724, 4.348593,
-0.8815095, 0.2134438, 2.654003, 2.087685, 0.7539754, -2.478812, 0.1475988,
-0.3185559, 2.039761, -1.921362, 0.1643196, 2.863641, 0.4538039, -1.488915,
0.7972866, 0.2992142, -0.4384953, 0.3609048, 2.380255, -0.7507977,
-1.005004, 1.511704, 0.212741, 1.051822, 2.172525, 0.2150965, 0.007859951,
3.08277, -0.1235147, -0.03427866, 0.4004827, 1.078061, -1.7794, 1.075778,
-0.6072247, -0.967762, 0.981806, 0.1475147, 0.4808787, 3.378195, 2.025399,
0.6688863, -1.168664, -0.1568437, -0.9180457, 2.605212, -1.593265, 0.98979,
0.01580698, 0.4365025, 1.70158, -3.126756, 0.3794898, -0.9738259, 1.51162,
1.183319, 1.78529, -2.180121, 2.644923, 0.7142706, 1.596689, -0.04979899,
-1.145537, 2.004128, -0.4103615, 0.6109707, 2.594508, -0.1496966,
-0.04674715, 0.8188964, 1.336248, 2.439359, -0.3677339, 2.413043,
0.4204059, 2.313515, 0.5516722, 1.182627, 2.005792, 0.6471613, 1.191033,
-0.7593308, 1.312155, -0.01918644, 2.773911, -0.7054297, 2.077788,
3.032506, 1.555499, 0.5449735, -1.605809, 0.6228236, 0.4814637, -1.703889,
-1.062384, -0.928541, -1.340148, 0.8033869, 0.8372255, 0.523864, -1.727313,
-2.037824, 2.480378, 1.277025, 0.06959089, 0.7103159, 0.0544758,
0.04918631, 1.200866, -0.1781491, 1.393799, 0.4857543, 2.062313,
0.4625032, 0.8548801, 2.290427, -0.6700734, 1.015888, 0.406015,
-0.1304835, 1.654307, 0.8550958, 0.8739146, 0.8685721, 0.5063582,
0.5741245, 1.059833, 1.467772, 1.625952, 0.8276773, 3.897082, 1.25006,
1.953879, 2.316474, 1.871225, 1.033222, -1.669686, -0.4284651, 1.650718,
0.2943459, 0.3820478, -0.02761709, 0.7161967, 2.162615, -1.49055, 1.835309,
0.4648584, -0.3689162, 1.28846, -2.474529, -0.2868397, 0.9725849, 1.351514,
0.8235514, 1.115832, 0.7304918, 0.7038161, -0.04599639, -0.1842319,
0.9880401, 0.6816606, 1.676341, 1.681047, 1.287988, -0.2473534, 0.1101533,
-0.4479058, 1.388329, -0.06226135, 0.866314, -0.3548175, 0.3684719, 3.09362,
1.217263, 0.8063339, -0.1454105, -2.064785, 0.6777007, 0.7040506, 2.354807,
-1.757202, 0.1417371, 0.8313071, -0.08294621, 1.461676, -0.7696986,
0.9176681, 0.4585622, -0.183079, -0.6166589, -0.2914721, 1.208298,
3.138118, 3.275939, -0.7958458, 0.08404926, -0.333922, 0.5868141, 4.781683,
0.548061, -0.742325, 1.110375, 0.05913553, -1.124493, 2.898643, 1.558618,
0.1342576, 2.450453, 0.5646837, 0.6724151, 0.09240844, 0.207641, 0.3514267,
1.662844, 1.588986, 0.09323521, -0.4878737, -0.2112836, 0.08405748, -1.00925,
-0.5610551, 1.021182, -0.1391414, 0.9960078, 0.2599478, 2.682325, 0.04613867,
-0.204634, 2.130909, 0.9686498, -1.223563, 0.2744176, 1.85641, -1.281635,
0.9568742, -1.529689, -1.674259, -0.3343894, 1.8089, 1.445891, -1.229222,
-0.9118662, 2.544743, -0.8193703, 1.005783, 0.4901241, -2.692732,
-0.9205373, 2.066589, 1.775053, -0.3033699, -0.6572719, -0.2016148,
0.4539527, 3.44007, 2.667011, -0.4534455, 1.541283, -2.217782, 0.7781816,
2.370952, -0.3569825, 1.635673, 0.5236256, 2.627279, 0.449129, -0.7842804,
0.3728755, 3.483306, -0.3741839, 0.0356962, 0.8522949, 2.924808, 0.9009428,
0.6442211, 0.4875439, -1.147957, 1.913263, -0.6915845, -0.1429951, 2.213266,
0.4237737, -0.3518183, -1.061589, -0.2981658, -1.247853, -0.231374,
-0.8824362, 0.6206775, 0.08903667, 0.8444898, 1.034736, 0.8028528,
0.6717759, 0.08211324, 3.398746, -0.2540745, 0.2478199, 1.861341,
-2.490341, -1.344672, -0.5838419, 2.34272, 1.763571, -2.024463,
-1.236073, -0.2419368, -1.630901, 0.7460867, -0.6292611, 2.096136,
-2.65242, 2.140336, -1.507524, 0.3395588, -0.05928033, 2.274768,
0.3861841, 0.5085077, -1.845912, 0.3248442, 0.4216029, 1.258335,
1.138112, 0.760768, 0.1332479, -0.6665008, 1.751383, -2.802195,
0.1859044, 0.4986566, 2.609326, 0.1801544, -0.5944885, -0.7516257,
2.67056, -0.4474156, 2.388389, 0.2167237, -2.322889, -0.4146484,
-0.399023, 0.08343873, 1.010352, 2.013957, 0.3069364, 1.536467,
0.9511184, -0.2019756, 0.3665186, -1.230914, -0.2192904, -3.563968,
2.30786, 1.767987, -1.285055, 1.946752, 1.651966, -0.0147004, 0.07518035,
1.519261, 0.1353215, 2.384335, 2.40277, 1.177133, 1.848385, 2.962562,
2.538202, -1.129364, -0.4316437, 2.227212, -1.562209, 1.009215, 0.7393541,
-1.453501, 1.414301, 0.5735626, 1.040577, -0.2106342, 0.3212164, 0.1065836,
-1.97112, 1.375393, 2.093986, 0.2135787, 2.190607, 0.5236311, -1.605053,
-1.367444, -2.755395, -3.671523, 0.4569495, 0.8198935, 1.767523,
-0.02109057, -0.6288153, 1.541511, -0.4063458, -1.252025, -1.394938,
-0.253524, 0.2050073, 0.9811099, -0.144743, 2.494582, -0.8237758,
0.8003133, 1.927059, -1.62744, -2.810295, -0.9012763, 0.1959343, 0.1912168,
0.2099924, 0.4692265, 1.599251, 0.1018631, 0.2383751, -2.480882, 1.411594,
-3.085389, 1.352404, 2.24788, 3.068772, 0.4020698, 2.813595, -1.460714,
-0.5061603, 1.812019, 3.225705, 1.470923,
-0.1067867, 1.060649, -0.3755217, 1.835892, 0.007699769, 0.472185,
-0.3891861, -0.8200396, 0.7980844, -0.5565764, 0.4594529, -1.418611,
-1.130325, 3.643348, 0.745943, -2.376944, 0.4900954, 0.7044373, 2.716719,
-2.92701, 3.055366, 0.4678358,-0.2200195, -1.538858, -0.7042396,
0.2906986, -0.4556325, -0.3156468, -0.9867677, -2.462476, 0.8334548,
0.01256107, 1.296663, 1.656972, 0.1890857, 0.4274917, -0.4149058,
0.7939621, 2.420778, 0.4551494, 0.5609379, 2.07608, -0.5984361,
-0.6234383, -0.7619428, 1.714608, 1.774031, 0.8078578, 2.493211,
-0.6436673, 0.8056096, 1.415049, 2.088208, -2.553487, 0.1474035,
2.468832, 0.3629936, 1.559848, -1.419344, 1.252219, 1.00223, 0.3505569,
-0.197483, 0.001481658, -1.154208, 0.07563953, 0.1254035, 2.082003,
3.085859, 0.2444306, -1.378354, 1.53788, 0.4830868, 0.5343908, 0.7835163,
0.1420364, -1.121353, 3.21487, 0.5545919, -1.23395, 2.29826, 2.087456,
-0.303342, -0.2842832, -1.959754, 0.2571105, 1.064723, -0.3268089,
-1.569037, -0.5811946, -0.5284372,
0.5773191, 2.896395, 0.7914887, -0.9262041, 1.074102, 1.987871,
-1.079295, -0.1175151, 1.119606, 2.646193, 1.150491, 0.1853689,
1.98902, 0.799424, 1.538916, 1.178183, 0.6577383, 1.175756, -1.728385,
1.996503, 1.06901, -0.1754592, 0.3988977, 1.51455, 3.310073, -0.08595954,
-1.317394, -0.4080714, 0.4559075,
-3.0631, -0.08753106, -1.545354, 2.032355, 1.775365, -2.556603,
0.8813956, -1.401756, 1.229163, 0.5330363, -1.161257, -0.4621782,
1.873578, -0.7765205, -0.3791675, 0.6822012, 0.3912565, 1.841402,
2.811005, 3.004356, 2.584023, 1.848531, 0.1702569, 0.8882989,
-0.9669123, 1.957959, 1.919071, 1.93756, 2.707397, -0.9161889,
0.9598898, 4.295957, 1.211699, -1.564695, 2.043878, -1.518748, 1.703412,
-1.963833, -2.188571, -2.581456, 3.29993, 2.085054, -2.430913, 0.593098,
1.179482, 1.515169, -1.377529, 0.06186947, -0.2119809, -1.20841, -0.05268589,
0.06202861, -0.3546809, 2.147835, 1.887589, -0.1841871, -0.5982248, 1.681687,
-1.242911, -0.9954123, 1.949604, -0.6246139, -2.056978, 1.190597, -0.5249918,
0.1899234, 1.444077, -0.2541717, -0.7115349, 0.05174258, -1.035978,
-0.2042441, -0.2449599, 0.8080922, -2.864996, 1.513828, 1.492188,
0.7552077, 0.1392787, -1.013541, -0.7631178, 0.7518061, 1.016008,
2.354008, 1.684694, -0.06316109, 1.92555, 2.981978, -1.2718, -0.1326985,
1.590285, 1.08895, 0.3074903, 1.238415, 1.578384, 0.3906198, -1.016455,
-0.1633113
),
y = c(
-1.297072, 0.9117613, 1.279106, -1.437861, 2.719283, 1.641577, 0.1372039,
-0.6868724, 1.763126, 3.530604, -1.776288, -3.704411, -2.15922, 2.390482,
0.9078989, 1.457612, -2.5354, -0.7839378, 0.2931226, -2.514011, 0.8324345,
0.1803356, -2.627092, 1.392182, -0.1179993, -0.2397086, -0.5089098, 2.452173,
0.3445466, -0.1147959, -0.8782663, -1.475184, 0.1268025, -1.296637, -0.9219598,
1.457447, 2.712058, -0.2403082, -0.3362178, -1.16605, -0.1455909, 1.876059,
-1.196383, 1.952799, 2.229562, -0.5173621, 0.04081391,
0.3159111, 0.2771666, -1.466228, 1.059278, 0.2178863, 0.2802811, 0.5816525,
-0.03047942, 0.2096255, -2.174504, 0.3895296, 5.434577, 1.139556, 0.8995658,
1.780634, -1.641323, -3.408731, -0.9886556, 0.3586492, 1.586885, 2.956389,
-0.3612072, 2.174704, -0.6647597, 0.7924472, -1.639424, 1.243475, -1.937213,
-0.8079536, 1.450924, -2.185047, 1.874988, 3.515306, 2.223285, -0.8352127,
-1.158314, 1.098762, -2.40215, 0.562364, 0.8520029, 0.7603164, 0.3739408,
1.980913, 3.435924, 2.148309, 0.07220071, 0.9115155, -0.9434633, -1.997795,
-1.199361, -1.991908, 1.41731, 1.688763, -0.7399537, 3.594597, -0.8912589,
3.31706, -2.563767, -1.231889, -0.8260225, -1.827058, -2.083749, -1.619199,
-1.505666, 1.122614, 1.37307, 2.845691, 1.666477, -4.840879, -0.8916903,
-2.625775, 0.8515742, 1.450746, -1.771652, -0.3089336, -0.5002624, -0.8376659,
-0.2661357, -1.550792, -1.056343, -1.866597, 1.318071, 1.630335,
3.51618, -3.200802, 1.613618, -0.09738592, -0.08220293, -1.228466, -4.895998,
1.288101, 2.999581, 0.6010791, -1.35872, -1.009735, 3.595655, -0.696191,
-1.647676, -0.2122697, -0.1542393, 1.330621, 0.619606, 0.02486573, -1.862943,
1.135191, 0.5034097, 2.694835, -3.049492, 1.657665, 0.7483125, -0.7797151,
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1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L,
0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L,
1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L,
0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L,
1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L,
1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L,
0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L,
0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L
)
),
class = "data.frame", row.names = c(NA, -1000L)
)
#### ratio.all ####
ratio.all <- structure(list(
g1 = c(
-0.00855060462516681, 0.0875322740300148,
0.135667161280393, 0.0675975772297497, -0.0630263399371546,
0.645198684809995, 0.645962479273306, 4.02797915540405, 0.3015
),
g2 = c(
0.256555129434397,
0.132507603823373, 0.493832589516168, 0.101532062423273, 0.519518425638448,
0.268324947839505, 0.288803238447459, 23.6566409119085, 0.101
),
g3 = c(
0.27784327303136, 0.131612923350767, 0.347578603049143,
0.101476089250621, 0.799368173397245, 0.378656574933523, 0.444798266157455,
11.7321774560372, 0.103
),
g4 = c(
0.171885629396363, 0.126538070268258,
0.0678804291494841, 0.0979836040168433, 2.5321824206185, 1.86413185440533,
4.10305056109213, 0.479934908102205, 0.1115
)
), class = "data.frame", row.names = c(
"by",
"byse", "bx", "bxse", "beta.ratio", "se.ratio.first", "se.ratio.second",
"fstat", "MAF"
))
#### diabetes_data ####
diabetes_data <- structure(list(
X = 1:38,
SNP = c(
"rs10184004", "rs10487796", "rs10748582", "rs10830963", "rs10965247",
"rs11671304", "rs11720108", "rs1215470", "rs1260326", "rs12910361",
"rs1317548", "rs1421085", "rs1496653", "rs1515110", "rs1800961",
"rs1801212", "rs2206277", "rs231361", "rs340882", "rs34872471",
"rs3802177", "rs3887925", "rs4239217", "rs464605", "rs4688760", "rs5213",
"rs697239", "rs7183842", "rs7258722", "rs72802365", "rs75199135",
"rs7646519", "rs7766070", "rs849142", "rs9267659", "rs9273363",
"rs9379084", "rs9667947"
),
beta.exposure = c(
-0.00340017, -0.00364702, -0.00471516, 0.00441241,
-0.00718076, 0.00319588, -0.00373061, -0.00439561, 0.00381735,
0.00317776, 0.00371348, 0.00519347, -0.00404417, 0.00355524,
0.00902729, 0.00497091, 0.00377017, 0.00352503, 0.00322908, 0.015002,
-0.00507762, 0.00306657, -0.00432418, 0.00334759, 0.00323006,
-0.00361201, -0.00360684, -0.00332443, -0.00293831, -0.00649326,
0.00311012, 0.00601842, 0.00623272, -0.00409952, 0.00493063,
0.00839501, -0.00605019, -0.00473778
),
beta.outcome = c(
-0.0121, -0.0237, -0.0029, 0.0105, -0.0246, -0.0094, -0.0313, 0.0191,
0.012, 0.021, 0.0049, -0.0094, -0.0144, -0.006, -0.0368, 0.0209,
0.0035, 0.0037, -0.0173, -0.0028, 0.0069, 0.004, -0.0185, 0.0233,
-0.0202, -0.0667, 0.0036, 0.019, -0.0272, 0.0531, 0.0084, 0.0285,
-0.0146, -5e-04, 0.0185, -0.0322, 0.0242, -0.0318
),
se.exposure = c(
0.000530106, 0.000523323, 0.000536941, 0.000582103, 0.000682275, 0.000558102,
0.000601993, 0.000575354, 0.000532551, 0.000575436, 0.000651396,
0.000530962, 0.0006452, 0.000541878, 0.00149065, 0.000578707,
0.00068194, 0.000601712, 0.000539394, 0.000572849, 0.000562996,
0.000524092, 0.000534239, 0.00059726, 0.000564126, 0.000547748,
0.000522317, 0.000582474, 0.000532197, 0.00097088, 0.000566935,
0.000560668, 0.000589995, 0.000520331, 0.000629621, 0.000566459,
0.000840322, 0.000710268
),
se.outcome = c(
0.0165, 0.0165, 0.0164,
0.0201, 0.0223, 0.0184, 0.0209, 0.0181, 0.0162, 0.018, 0.0198,
0.0165, 0.0198, 0.0168, 0.044, 0.0174, 0.0214, 0.0189, 0.0166,
0.0172, 0.0172, 0.016, 0.0176, 0.0188, 0.0174, 0.0164, 0.0162,
0.0176, 0.0173, 0.029, 0.017, 0.0169, 0.0187, 0.0161, 0.0216,
0.0199, 0.0279, 0.022
)
), class = "data.frame", row.names = c(NA, -38L))
```
---
# Ratio Method
To apply the ratio method, we will use the dataset `coursedata` (individual level data) providing phenotypes (`x`and `y`) and genotypes (`g` columns).
```{r head_dataset1}
head(coursedata)
```
## Ratio estimates for variant j
$$\theta_{Y_j} = \frac{\hat{\beta}_{Y_j}}{\hat{\beta}_{X_j}}$$
Where `j = 1` in this example.
```{r ratio-method-beta}
# Genetic association with the outcome
by1 <- lm(formula = y ~ g1, data = coursedata)$coef[2]
# Genetic association with the exposure
bx1 <- lm(formula = x ~ g1, data = coursedata)$coef[2]
beta.ratio1 <- by1 / bx1
cat("Ratio estimate for g1 =", beta.ratio1)
```
## Standard error of ratio estimate (first order)
$$se_{first}(\theta_{Y_j}) = \frac{se(\hat{\beta}_{Y_j})}{|\hat{\beta}_{X_j}|}$$
```{r ratio-method-se-first}
# Standard error of the G-Y association
byse1 <- summary(lm(formula = y ~ g1, data = coursedata))$coef[2, 2]
se.ratio1first <- byse1 / sqrt(bx1^2)
## sqrt(bx1^2) => because se is always positive.
cat("Standard error (first order) of the ratio estimate g1 =", se.ratio1first)
```
## Standard error of ratio estimate (second order)
$$se_{second}(\theta_{Y_j}) = \sqrt{\frac{se(\hat{\beta}_{Y_j})^2}{\hat{\beta}_{X_j}^2} + \frac{\hat{\beta}_{Y_j}^2 se(\hat{\beta}_{X_j})^2}{\hat{\beta}_{X_j}^4}}$$
```{r ratio-method-se-second}
# Standard error of the G-X association
bxse1 <- summary(lm(formula = x ~ g1, data = coursedata))$coef[2, 2]
se.ratio1second <- sqrt(byse1^2 / bx1^2 + by1^2 * bxse1^2 / bx1^4)
cat("Standard error (second order) of the ratio estimate g1 =", se.ratio1second)
```
## F-statistic
The F-statistic from the regression of the risk factor on the genetic variant(s) is used as a measure of 'weak instrument bias', with smaller values suggesting that the estimate may suffer from weak instrument bias.
For instance, some studies recommend excluding genetic variants if they have a F-statistic less than 10.
```{r ratio-method-Fstat}
fstat1 <- summary(lm(formula = x ~ g1, data = coursedata))$f[1]
cat("F-stat =", fstat1)
```
## Ratio estimates for a Binary Outcome
In a case control setting, it is usual to regress the risk factor on the genetic variant in controls only because the case-control sample is generally unrepresentative of the population and if measurements of the risk factor are made post-disease = Avoid possible reverse causation.
```{r bin-ratio-g1}
# logistic regression for gene-outcome association
by1.bin <- glm(formula = y.bin ~ g1, data = coursedata, family = binomial)$coef[2]
byse1.bin <- summary(glm(formula = y.bin ~ g1, data = coursedata, family = binomial))$coef[2, 2]
# linear regression in the controls only
# bx1.bin <- lm(coursedata$x[coursedata$y.bin==0]~coursedata$g1[coursedata$y.bin ==0])$coef[2]
bx1.bin <- lm(x ~ g1, data = coursedata[coursedata$y.bin == 0, ])$coef[2]
beta.ratio1.bin <- by1.bin / bx1.bin
# beta.ratio1.bin #ratio estimate for g1
cat("Ratio estimate for g1 with binary outcome =", beta.ratio1.bin, "\n")
se.ratio1.bin <- byse1.bin / bx1.bin
# se.ratio1.bin #standard error of the ratio estimate for g1
cat("Standard error of the ratio estimate for g1 with binary outcome =", se.ratio1.bin)
```
---
# Two-Stage Least Squares Method (TSLS) Method
When multiple variants G as Instrumental Variables, **TSLS** (or **2SLS**) is performed by:
$$X \sim G_1 + G_{.\ .\ .} + G_j$$
_i.e._ regressing the risk factor on all the genetic variants in the same model, and storing the fitted values of the risk factor. And
$$Y \sim \hat{X}$$
_i.e._ a regression is then performed with the outcome on the fitted values of the risk factor.
## Estimates
**By hand** we can make the computation as follow (but it is not recommanded !)
```{r TSLS-byhand}
by.hand.fitted.values <- lm(x ~ g1 + g2 + g3 + g4, data = coursedata)$fitted
by.hand <- lm(coursedata$y ~ by.hand.fitted.values)
cat("TSLS estimate =", summary(by.hand)$coef[2], "\n")
cat("TSLS standard error =", summary(by.hand)$coef[2, 2])
```
Performing two-stage least squares by hand is generally discouraged, as the standard error in the second-stage of the regression does not take into account uncertainty in the first-stage regression. The R package `{ivpack}` performs the **two-stage least squares method** using the `ivreg` function:
```{r TSLS-ivreg}
# library(ivpack) ## Depends AER for ivreg
ivmodel.all <- ivreg(y ~ x | g1 + g2 + g3 + g4, x = TRUE, data = coursedata)
# 2SLS estimates
cat("TSLS estimate =", summary(ivmodel.all)$coef[2], "\n")
cat("TSLS standard error =", summary(ivmodel.all)$coef[2, 2])
```
## F-statistic
```{r TSLS-fstat}
cat("F-stat =", summary(lm(x ~ g1 + g2 + g3 + g4, data = coursedata))$f[1])
```
## TSLS Estimates for a Binary Outcome with g1
First stage with a binary outcome is only conducted on controls, second step is done with a logistic regression.
```{r TSLS-bin-g1}
# values for g1 in the controls only
g1.con <- coursedata$g1[coursedata$y.bin == 0]
# values for the risk factor in the controls only
x.con <- coursedata$x[coursedata$y.bin == 0]
# Generate predicted values for all participants based on the linear regression in the controls only :
predict.con.g1 <- predict(lm(x.con ~ g1.con), newdata = list(g1.con = coursedata$g1))
# Fit a logistic regression model on all the participants
tsls1.con <- glm(coursedata$y.bin ~ predict.con.g1, family = binomial)
cat("TSLS estimate for a binary outcome =", summary(tsls1.con)$coef[2], "\n")
cat("TSLS standard error for a binary outcome =", summary(tsls1.con)$coef[2, 2])
```
Observation : With a single instrumental genetic variation, TSLS and Ratio method give identical estimates.
`beta.ratio1.bin = summary(tsls1.con)$coef[2]`
## TSLS Estimates for a Binary Outcome with all G
With multi genetic variants as IV, the TSLS estimate is a weighted average of the ratio estimates.
```{r TSLS-bin-allg}
# g1.con <- coursedata$g1[coursedata$y.bin == 0] # values for g2 in the controls only
g2.con <- coursedata$g2[coursedata$y.bin == 0] # values for g2 in the controls only
g3.con <- coursedata$g3[coursedata$y.bin == 0] # values for g3 in the controls only
g4.con <- coursedata$g4[coursedata$y.bin == 0] # values for g4 in the controls only
predict.con <- predict(
lm(x.con ~ g1.con + g2.con + g3.con + g4.con), # Predicted values
newdata = c(
list(g1.con = coursedata$g1),
list(g2.con = coursedata$g2),
list(g3.con = coursedata$g3),
list(g4.con = coursedata$g4)
)
)
# Logistic regression
tsls1.con.all <- glm(coursedata$y.bin ~ predict.con, family = binomial)
cat("TSLS estimate for a binary outcome =", summary(tsls1.con.all)$coef[2], "\n")
cat("TSLS standard error for a binary outcome =", summary(tsls1.con.all)$coef[2, 2])
```
+ Questions ?
Does ivreg function work for binary outcome ? like `ivmodel.all.bin = ivreg(y.bin~x|g1+g2+g3+g4, x = TRUE)`
Answer : So the function works with a binary trait, in that it will run and give you an answer. But it doesn't treat the trait as special because it is binary and the estimate just represents the absolute change in the exposure as it is a continuous variable. Hence if you want an odds ratio or a relative risk estimate, we suggest to use the two-stage method. Provided that genetic instruments are strong, the overprecision due to not accounting for uncertainty in the first-stage regression is typically small, even negligible.
---
# Inverse Variance Weighted (IVW) Method
We can use **summarized data** (the genetic associations with the risk factor and with the outcome, with their standard errors) to estimate the causal effect of the risk factor on the outcome via the **inverse-variance weighted (IVW) method**.
## IVW Estimates (by hand)
To do so, we will use the dataset `ratio.all`,
```{r ratio-all-data}
ratio.all
bx <- ratio.all["bx", ]
by <- ratio.all["by", ]
bxse <- ratio.all["bxse", ]
byse <- ratio.all["byse", ]
```
and apply following formulas :
$$\hat{\theta}_{IVW} = \frac{\sum_{j=1}^{n} \hat{\theta}_j /var(\hat{\theta}_j)}{\sum_{j=1}^{n} 1 / var(\hat{\theta}_j)}$$
$$var(\hat{\theta}_j) = \frac{var(\beta_{Y_j})}{\beta^2_{X_j}} = \frac{\sigma^2_{Y_j}}{\beta^2_{X_j}}$$
```{r ivw-estimate}
beta.ivw <- sum(bx * by * byse^-2) / sum(bx^2 * byse^-2)