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

calc_myreg_mreg_logistic_yreg_logistic(
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.