grr {sensmediation}

Analytic gradients of the loglikelihood functions for ML estimation of regression parameters

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