LogL: Implementation of loglikelihood functions for ML estimation...

In sensmediation: Parametric Estimation and Sensitivity Analysis of Direct and Indirect Effects

Implementation of loglikelihood functions for ML estimation of regression parameters

Description

Implementation of loglikelihood functions for ML estimation of regression parameters for different combinations of exposure, mediator and outcome models. The functions are named according to the convention LogL."model.expl type""model.resp type" where b stands for binary probit regression and c stands for linear regression.

Usage

LogL.bb(
  par,
  Rho,
  X.expl = X.expl,
  X.resp = X.resp,
  outc.resp = outc.resp,
  outc.expl = outc.expl
)

LogL.bc(
  par,
  Rho,
  X.expl = X.expl,
  X.resp = X.resp,
  outc.resp = outc.resp,
  outc.expl = outc.expl
)

LogL.cb(
  par,
  Rho,
  X.expl = X.expl,
  X.resp = X.resp,
  outc.resp = outc.resp,
  outc.expl = outc.expl
)

LogL.cc(
  par,
  Rho,
  X.expl = X.expl,
  X.resp = X.resp,
  outc.resp = outc.resp,
  outc.expl = outc.expl
)

Arguments

par	Vector of parameter values.
Rho	The value of the sensitivity parameter.
X.expl	The model matrix (see model.matrix) of model.expl
X.resp	The model matrix (see model.matrix) of model.resp
outc.resp	The outcome of model.resp, a vector.
outc.expl	The outcome of model.expl, a column matrix.

See Also

coefs.sensmed, maxLik

sensmediation documentation built on Sept. 27, 2024, 1:06 a.m.