scaled_neg_log_gradient: Routine for computing the negative log-posterior gradient for...

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

Description

Routine for computing the negative log-posterior gradient for the MASSIVE model

Usage

scaled_neg_log_gradient(
  J,
  N,
  SS,
  sigma_G,
  param_list,
  prior_sd,
  n = 2,
  log_scale = TRUE
)

Arguments

J	Integer number of genetic instrumental variables.
N	Integer number of observations.
SS	Numeric matrix containing first- and second-order statistics.
sigma_G	Numeric vector of genetic IV standard deviations.
param_list	List of IV model parameter values.
prior_sd	List of standard deviations for the parameter Gaussian priors.
n	Integer number of alleles (trials) for the binomial genetic variants.
log_scale	Logical flag indicating whether scale parameters (sigma_X, sigma_Y) are given on the log-scale or on the normal scale.

Value

numeric vector; negative log-posterior gradient for the MASSIVE model.

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