derive_smart_starting_points: Routine for computing smart starting points from the...

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

Description

Routine for computing smart starting points from the sufficient statistics.

Usage

derive_smart_starting_points(SS, sigma_G = binomial_sigma_G(SS))

Arguments

SS	Numeric matrix containing first- and second-order statistics.
sigma_G	Numeric vector of instrument standard deviations.

Value

List containing three smart starting points on the ML manifold for the posterior optimization.

Examples

J <- 5 # number of instruments
N <- 1000 # number of samples
parameters <- random_Gaussian_parameters(J) 
EAF <- runif(J, 0.1, 0.9) # EAF random values
dat <- generate_data_MASSIVE_model(N, 2, EAF, parameters)
derive_smart_starting_points(dat$SS)

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