BootEstimation_for: Bootstrapping Estimation for Causal Mediation Effects via...

In unvs.med: A Universal Approach for Causal Mediation Analysis

Bootstrapping Estimation for Causal Mediation Effects via Ordinary "for" Loop

Description

This function obtains the estimates of mediation effects by the ordinary for loop. Through bootstrap sampling and repeating the algorithm of function SingleEstimation, This function obtains a number of estimates for each type of effect.

This is an internal function, automatically called by the function Statistics.

Usage

BootEstimation_for (m_model, y_model, data, X, M, Y,
m_type, y_type, boot_num = 100)

Arguments

m_model	a fitted model object for the mediator.
y_model	a fitted model object for the outcome.
data	a dataframe used in the analysis.
X	a character variable of the exposure's name.
M	a character variable of the mediator's name.
Y	a character variable of the outcome's name.
m_type	a character variable of the mediator's type.
y_type	a character variable of the outcome's type.
boot_num	the times of bootstrapping in the analysis. The default is 100.

Details

This function is realized by the ordinary for loop, therefore may take longer time to proceed. For small amounts of data, e.g., dozens to a hundred samples, with relatively simple models, for loop is recommended.

Value

This function returns a list of three dataframes, i.e., the bootstrapping results of the mediation effects. This list is also saved in the return of the main function FormalEstmed.

unvs.med documentation built on June 8, 2025, 10:15 a.m.