MVmr_cML: MVMRcML method with Data Perturbation
In MendelianRandomization: Mendelian Randomization Package

MVmr_cML

R Documentation

MVMRcML method with Data Perturbation

Description

This is the internal MVMRcML-BIC function of mr_mvcML.

Usage

MVmr_cML(
  b_exp,
  b_out,
  se_bx,
  Sig_inv_l,
  n,
  K_vec = as.numeric(c()),
  random_start = 1L,
  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.
`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
l_vec: A vector of negative log-likelihood corresponding to each K.
K_vec: A vector of candidate K's
theta_vec: A matrix of causal parameter estimates, each column corresponds to a candidate K.
Conv_vec: A vector of successful convergence indicators corresponding to each K.
Converge: Indicator of successful convergence, 0 means success, 1 means failure.
BIC_vec: Data perturbation with successful convergence
Khat: The length of BIC_invalid.