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

View source: R/fns.R

rand.eff.unpenalizedR Documentation

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 July 26, 2023, 6:11 p.m.