grr: Analytic gradients of the loglikelihood functions for ML...

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

Description

Implementation of the analytic gradients of the 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 grr."model.expl type""model.resp type" where b stands for binary probit regression and c stands for linear regression.

Usage

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

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

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

grr.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 June 3, 2019, 9:02 a.m.