constrained.unpenalized: Estimate hard constraint model parameters using cyclical...

In messi: Mediation with External Summary Statistic Information

Estimate hard constraint model parameters using cyclical coordinate descent.

Description

Estimate hard constraint model parameters using cyclical coordinate descent.

Usage

constrained.unpenalized(
  Y,
  M,
  A,
  C = NULL,
  T.hat.external,
  err.tol.out = 1e-08,
  err.tol.med = 1e-08,
  max.itr = 10000
)

Arguments

Y	A (n x 1) continuous outcome vector.
M	A (n x p_m) matrix of mediators.
A	A (n x 1) vector of exposures.
C	A (n x p_c) matrix of confounders and adjustment covariates. If there are no confounders or adjustment covariates set C = NULL.
T.hat.external	External estimate of the total effect.
err.tol.out	Termination condition for cyclical coordinate descent algorithm with respect to the outcome model parameters.
err.tol.med	Termination condition for cyclical coordinate descent algorithm with respect to the mediator model parameters.
max.itr	Maximum number of iterations for cyclical coordinate descent algorithm.

Value

A list containing point estimates of the hard constraint model parameters and an indicator of whether the algorithm converges.

messi documentation built on April 3, 2025, 7:40 p.m.