BF_distr

This function is used to generate bootstrap samples for the bootstrapping Fieller's confidence interval.

The penalized inverse-variance weighted (pIVW) estimator is a Mendelian randomization method for estimating the causal effect of an exposure variable on an outcome of interest based on summary-level GWAS data. The pIVW estimator accounts for weak instruments and balanced horizontal pleiotropy simultaneously. See Xu S., Wang P., Fung W.K. and Liu Z. (2022) <doi:10.1111/biom.13732>.

Siqi Xu

mr.pivw

Penalized Inverse-Variance Weighted Estimator for Mendelian
Randomization

BF_distr function

<dl> <dt>data</dt>
<dd>A matrix or data frame consists of four columns: the 1st (2nd) column contains the estimated genetic effects on the outcome (exposure); the 3rd (4th) column contains the estimated standard errors of the estimated genetic effects on the outcome (exposure).</dd> <dt>beta_hat</dt>
<dd>The estimated causal effect.</dd> <dt>tau2</dt>
<dd>The estimated variance of the horizontal pleiotropy.</dd> <dt>lambda</dt>
<dd>The penalty parameter in the pIVW estimator. The penalty parameter plays a role in the bias-variance trade-off of the estimator.</dd> <dt>n_boot</dt>
<dd>The sample size of the bootstrap samples. By default, n_boot=1000.</dd> <dt>seed_boot</dt>
<dd>The seed for random sampling in the bootstrap method. By default, seed_boot=1.</dd></dl>

Arguments

Generate bootstrap samples for the bootstrapping Fieller's confidence interval — BF_distr

<dl>

 <dt>data</dt>
<dd>A matrix or data frame consists of four columns: the 1st (2nd) column contains the estimated genetic effects on the outcome (exposure); the 3rd (4th) column contains the estimated standard errors of the estimated genetic effects on the outcome (exposure).</dd>

 <dt>beta_hat</dt>
<dd>The estimated causal effect.</dd>

 <dt>tau2</dt>
<dd>The estimated variance of the horizontal pleiotropy.</dd>

 <dt>lambda</dt>
<dd>The penalty parameter in the pIVW estimator. The penalty parameter plays a role in the bias-variance trade-off of the estimator.</dd>

 <dt>n_boot</dt>
<dd>The sample size of the bootstrap samples. By default, n_boot=1000.</dd>

 <dt>seed_boot</dt>
<dd>The seed for random sampling in the bootstrap method. By default, seed_boot=1.</dd>

</dl>

BF_distr: Generate bootstrap samples for the bootstrapping Fieller's confidence interval

Description

Usage

Value

Arguments