do_sim_sequentialPAF: Simulates a Fitted Model for a Mediator or Exposure or Risk...

In MauriceOConnell/causalPAF: Causal Effect for Population Attributable Fractions (PAF)

Simulates a Fitted Model for a Mediator or Exposure or Risk Factor Allowing for Potential Outcomes in Causal Analysis

Description

A fitted model for a mediator or exposure or risk factor can be simulated given values of the other risk factors or exposure saved in the data frame current_mat. This allows for potential outcomes to be measured for causal analysis. For example, for an outcome Y_{A,M} with exposure A and mediators M_{1}, M_{3}, … M_{K} the function can measure potential outcomes such as Y_{A=0,M_{1},M_{2},M_{3}} or Y_{A=0,M_{1},M_{2}=0,M_{3}=0} when there are three mediators. The model can be either a binary, continuous or an ordered factor response model.

Usage

do_sim_sequentialPAF(colnum, current_mat, model)

Arguments

colnum	Column number of exposure or risk factor of interest within the data frame. The data frame has cases in rows and variables in columns.
current_mat	The data frame containing the data for which the model can be simulated with. For potential outcomes for example such as Y_{A=0,M_{1},M_{2},M_{3}} requires the exposure in this case to be pre set to zero i.e. current_mat should have the exposure Y_{A=0} set to zero if simulating e.g. M_{1}.
model	A fitted causal regression model for either a binary, continuous or an ordered factor response.

Value

simulation

simulation

Examples

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MauriceOConnell/causalPAF documentation built on Aug. 20, 2022, 4:27 a.m.