Create calculators for effects and se (mreg linear / yreg linear)

Construct functions for the conditional effect estimates and their standard errors in the mreg linear / yreg linear setting. Internally, this function deconstructs model objects and feeds parameter estiamtes to the internal worker functions calc_myreg_mreg_linear_yreg_linear_est and calc_myreg_mreg_linear_yreg_linear_se.

calc_myreg_mreg_linear_yreg_linear(
  mreg,
  mreg_fit,
  yreg,
  yreg_fit,
  avar,
  mvar,
  cvar,
  emm_ac_mreg,
  emm_ac_yreg,
  emm_mc_yreg,
  interaction
)

Arguments

mreg

A character vector of length 1. Mediator regression type: "linear" or "logistic".

mreg_fit

Model fit from fit_mreg

yreg

A character vector of length 1. Outcome regression type: "linear", "logistic", "loglinear", "poisson", "negbin", "survCox", "survAFT_exp", or "survAFT_weibull".

yreg_fit

Model fit from fit_yreg

avar

A character vector of length 1. Treatment variable name.

mvar

A character vector of length 1. Mediator variable name.

cvar

A character vector of length > 0. Covariate names. Use NULL if there is no covariate. However, this is a highly suspicious situation. Even if avar is randomized, mvar is not. Thus, there are usually some confounder(s) to account for the common cause structure (confounding) between mvar and yvar.

emm_ac_mreg

A character vector of length > 0. Effect modifiers names. The covariate vector in treatment-covariate product term in the mediator model.

emm_ac_yreg

A character vector of length > 0. Effect modifiers names. The covariate vector in treatment-covariate product term in the outcome model.

emm_mc_yreg

A character vector of length > 0. Effect modifiers names. The covariate vector in mediator-covariate product term in outcome model.

interaction

A logical vector of length 1. The presence of treatment-mediator interaction in the outcome model. Default to TRUE.

Value

A list containing a function for effect estimates and a function for corresponding standard errors.