mr.raps: Main function
In mr.raps: Two Sample Mendelian Randomization using Robust Adjusted Profile Score

Description Usage Arguments Value Functions References Examples

mr.raps is the main function.

mr.raps.all: Quick analysis with all six methods

mr.raps.simple: No overdispersion, l2 loss

mr.raps.overdispersed: Overdispersion, l2 loss

mr.raps.simple.robust: No overdispersion, robust loss

mr.raps.overdispersed.robust: Overdispersed, robust loss

mr.raps(b_exp, b_out, se_exp, se_out, over.dispersion = FALSE,
  loss.function = c("l2", "huber", "tukey"), diagnosis = FALSE,
  se.method = c("sandwich", "bootstrap"), k = switch(loss.function[1], l2 =
  NULL, huber = 1.345, tukey = 4.685), B = 1000, suppress.warning = FALSE)

mr.raps.all(b_exp, b_out, se_exp, se_out)

mr.raps.simple(b_exp, b_out, se_exp, se_out, diagnosis = FALSE)

mr.raps.overdispersed(b_exp, b_out, se_exp, se_out,
  initialization = c("simple", "mode"), suppress.warning = FALSE,
  diagnosis = FALSE, niter = 20, tol = .Machine$double.eps^0.5)

mr.raps.simple.robust(b_exp, b_out, se_exp, se_out, loss.function = c("huber",
  "tukey"), k = switch(loss.function[1], huber = 1.345, tukey = 4.685),
  diagnosis = FALSE)

mr.raps.overdispersed.robust(b_exp, b_out, se_exp, se_out,
  loss.function = c("huber", "tukey"), k = switch(loss.function[1], huber =
  1.345, tukey = 4.685), initialization = c("l2", "mode"),
  suppress.warning = FALSE, diagnosis = FALSE, niter = 20,
  tol = .Machine$double.eps^0.5)

`b_exp`	A vector of SNP effects on the exposure variable, usually obtained from a GWAS.
`b_out`	A vector of SNP effects on the outcome variable, usually obtained from a GWAS.
`se_exp`	A vector of standard errors of `b_exp`.
`se_out`	A vector of standard errors of `b_out`.
`over.dispersion`	Should the model consider overdispersion (systematic pleiotropy)? Default is FALSE.
`loss.function`	Either the squared error loss (`l2`) or robust loss functions/scores (`huber` or `tukey`).
`diagnosis`	Should the function returns diagnostic plots and results? Default is FALSE
`se.method`	How should the standard error be estimated? Either by sandwich variance formula (default and recommended) or the bootstrap.
`k`	Threshold parameter in the Huber and Tukey loss functions.
`B`	Number of bootstrap resamples
`suppress.warning`	Should warning messages be suppressed?
`initialization`	Method to initialize the robust estimator. "Mode" is not supported currently.
`niter`	Maximum number of interations to solve the estimating equations.
`tol`	Numerical precision.

A list

beta.hat: Estimated causal effect
beta.se: Standard error of beta.hat
beta.p.value: Two-sided p-value of beta.hat
tau2.hat: Overdispersion parameter if over.dispersion = TRUE
tau2.se: Standard error of tau2.hat
std.resid: Standardized residuals of each SNP, returned if diagnosis = TRUE
beta.hat.loo: Leave-one-out estimates of beta.hat, returned if diagnosis = TRUE
beta.hat.bootstrap: Median of the bootstrap estimates, returned if se.method = "bootstrap"
beta.se.bootstrap: Median absolute deviation of the bootstrap estimates, returned if se.method = "bootstrap"

mr.raps.all:
mr.raps.simple:
mr.raps.overdispersed:
mr.raps.simple.robust:
mr.raps.overdispersed.robust:

Qingyuan Zhao, Jingshu Wang, Jack Bowden, Dylan S. Small. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. https://arxiv.org/abs/1801.09652.

data(bmi.sbp)
attach(bmi.sbp)

## All estimators
mr.raps.all(beta.exposure, beta.outcome, se.exposure, se.outcome)

## Diagnostic plots
res <- mr.raps(beta.exposure, beta.outcome, se.exposure, se.outcome,
diagnosis = TRUE)
res <- mr.raps(beta.exposure, beta.outcome, se.exposure, se.outcome,
TRUE, diagnosis = TRUE)
res <- mr.raps(beta.exposure, beta.outcome, se.exposure, se.outcome,
TRUE, "tukey", diagnosis = TRUE)

detach(bmi.sbp)

data(bmi.bmi)
attach(bmi.bmi)

## Because both the exposure and the outcome are BMI, the true "causal" effect should be 1.

## All estimators
mr.raps.all(beta.exposure, beta.outcome, se.exposure, se.outcome)

detach(bmi.bmi)