rand.eff.unpenalized: Estimate soft constraint model parameters using the EM...

In messi: Mediation with External Summary Statistic Information

Estimate soft constraint model parameters using the EM algorithm.

Description

Estimate soft constraint model parameters using the EM algorithm.

Usage

rand.eff.unpenalized(
  Y,
  M,
  A,
  C = NULL,
  rand.eff.mean,
  rand.eff.var,
  T.hat.external = T.hat.external,
  var.T.hat.external = var.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.
rand.eff.mean	Mean of the random effects distribution for the internal total effect parameter.
rand.eff.var	Variance of the random effects distribution for the internal total effect parameter.
T.hat.external	External estimate of the total effect.
var.T.hat.external	Estimated variance of the external total effect estimator.
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 soft constraint model parameters and an indicator of whether the algorithm converges.

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