mediationking: Mediating Analysis

In rSPARCS: Sites, Population, and Records Cleaning Skills

Mediating Analysis

Description

This function provides convenient algorithm to calculate total effect, mediation effect, direct effect and the proportion of mediation effect.

Usage

mediationking(dataset,outcome,mediator,exposure,n.sim)

Arguments

dataset	The dataset that is used for analysis.
outcome	The name of the outcome variable in the dataset.
mediator	The name of the mediator in the dataset.
exposure	The name of the exposure factor in the dataset.
n.sim	Times of simulation to estimate 95% confidence intervals.

Details

Please use set.seed() if you want to get a consistent result; this function will be expended to allow more covariates shortly.

Value

Total effect	The total effect of the exposure on the outcome variable.
Indirect effect	The effect of the exposure on the outcome variable that is caused by mediator.
Direct effect	The effect of the exposure on the outcome variable that is caused by factors other than the mediator.
Meditation.proportion	The proportion of the mediation effect.

Examples

set.seed(1)
exposure<-rnorm(20,0,1)
mediator<-rnorm(20,10,1)
outcome<-rnorm(20,10,1)
dataset<-data.frame(outcome,mediator,exposure)
mediationking(dataset,"outcome","mediator","exposure")

rSPARCS documentation built on Nov. 21, 2023, 9:07 a.m.