mr.raps.mle: Main function for RAPS (MLE weights)

In qingyuanzhao/mr.raps: Two Sample Mendelian Randomization using Robust Adjusted Profile Score

Main function for RAPS (MLE weights)

Description

Main function for RAPS (MLE weights)

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

Usage

mr.raps.mle(
  b_exp,
  b_out,
  se_exp,
  se_out,
  over.dispersion = FALSE,
  loss.function = c("l2", "huber", "tukey"),
  diagnostics = FALSE,
  pruning = TRUE,
  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.mle.all(b_exp, b_out, se_exp, se_out)

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

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

Arguments

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).
diagnostics	Should the function returns diagnostic plots and results? Default is FALSE
pruning	Should the function remove unusually large se_exp?
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.

Details

mr.raps.mle is the main function for RAPS. It is replaced by the more general and robust function mr.raps.shrinkage.

Value

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 diagnostics = TRUE

beta.hat.loo

Leave-one-out estimates of beta.hat, returned if diagnostics = 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"

Functions

• mr.raps.mle.all():

• mr.raps.simple():

• mr.raps.overdispersed():

• mr.raps.simple.robust():

• mr.raps.overdispersed.robust():

References

Qingyuan Zhao, Jingshu Wang, Gibran Hemani, 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.

Examples

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

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

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

detach(bmi.bmi)

qingyuanzhao/mr.raps documentation built on June 13, 2025, 12:38 p.m.