MVmr_cML_DP: MVMRcML method with Data Perturbation

In MendelianRandomization: Mendelian Randomization Package

MVMRcML method with Data Perturbation

Description

This is the internal MVMRcML-DP function of mr_mvcML.

Usage

MVmr_cML_DP(
  b_exp,
  b_out,
  se_bx,
  Sig_inv_l,
  n,
  K_vec = as.numeric(c()),
  random_start = 1L,
  num_pert = 100L,
  min_theta_range = -0.5,
  max_theta_range = 0.5,
  maxit = 100L,
  thres = 1e-04
)

Arguments

b_exp	A m*L matrix of SNP effects on the exposure variable.
b_out	A m*1 matrix of SNP effects on the outcome variable.
se_bx	A m*L matrix of standard errors of b_exp.
Sig_inv_l	A list of the inverse of m covariance matrices.
n	The smallest sample size of the L+1 GWAS dataset.
K_vec	Sets of candidate K's, the constraint parameter representing number of invalid IVs.
random_start	Number of random start points, default is 1.
num_pert	Number of perturbation, default is 100.
min_theta_range	The lower bound of the uniform distribution for each initial value for theta generated from.
max_theta_range	The upper bound of the uniform distribution for each initial value for theta generated from.
maxit	Maximum number of iterations for each optimization, default is 100.
thres	Threshold for convergence criterion.

Value

A list

BIC_theta

Estimated causal effect from MVMR-cML-BIC

BIC_invalid

Invalid IVs selected by MVMR-cML-BIC

BIC_DP_theta

Estimated causal effect from MVMR-cML-DP

BIC_DP_se

Estimate standard error for BIC_DP_theta

eff_DP_B

Data perturbation with successful convergence

DP_ninvalid

A vector of the number of selected invalid IVs by MVMRcML-BIC in each data perturbation.

MendelianRandomization documentation built on Aug. 9, 2023, 1:05 a.m.