MR_regression_coefficients_to_moments: Function to extract first-order and second-order estimated...

In igbucur/MASSIVE: MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models

Description

Function to extract first-order and second-order estimated moments from summary data.

Usage

MR_regression_coefficients_to_moments(
  J,
  beta_XG,
  sigma_XG,
  nobs_XG,
  beta_YG,
  sigma_YG,
  nobs_YG,
  beta_YX,
  EAF = rep(0.5, J),
  n = 2
)

Arguments

J	Integer number of instrumental variables.
beta_XG	Numeric vector of estimated G -> X regression terms.
sigma_XG	Numeric vector of estimated G -> X standard errors.
nobs_XG	Integer sample size in G -> X regression (GWA) study.
beta_YG	Numeric vector of estimated G -> Y regression terms.
sigma_YG	Numeric vector of estimated G -> Y standard errors.
nobs_YG	Integer sample size in G -> Y regression (GWA) study.
beta_YX	Numeric vector of estimated X -> Y regression value.
EAF	Numeric vector of effect allele frequencies
n	Integer number of alleles (trials) for binomial genetic variant.

Value

Numeric matrix of size (J+3)x(J+3) containing the first-order and second-order moments of the (J+2) vector (G, X, Y).

Examples

MR_regression_coefficients_to_moments(1, 1, 1e-3, 1000, 1, 1e-3, 1000, 1, 1e-3)

igbucur/MASSIVE documentation built on Oct. 26, 2020, 1:26 a.m.