determine_hyperparameters: Functioning for automatically determining MASSIVE...

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

Description

Functioning for automatically determining MASSIVE hyperparameters from data, where we assume a N(0, 10) prior on skappa_X.

Usage

determine_hyperparameters(J, N, SS, sigma_G)

Arguments

J	Integer number of candidate instruments.
N	Integer number of observations.
SS	Numeric matrix containing first- and second-order statistics.
sigma_G	Numeric vector of instrument standard deviations.

Value

List containing sd_slab and sd_spike hyperparameters, indicating the standard deviation of the slab and spike component, respectively.

Examples

J <- 5 # number of instruments
N <- 1000 # number of samples
par <- random_Gaussian_parameters(J)
dat <- generate_data_MASSIVE_model(N, n = 2, p = rep(0.3, J), par)
determine_hyperparameters(J, N, dat$SS, binomial_sigma_G(dat$SS))

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