Related Experiment Video
Updated: Jun 2, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
RMediation: an R package for mediation analysis confidence intervals.
Davood Tofighi1, David P MacKinnon
1School of Psychology, Georgia Institute of Technology, J.S. Coon Bldg. 654 Cherry Street, Atlanta, GA 30332-0170, USA. dtofighi@psych.gatech.edu
The RMediation package provides improved confidence intervals (CIs) for mediated effects using the distribution-of-the-product method. This R software offers accurate and accessible CI calculations, overcoming limitations of previous tools like PRODCLIN.
Area of Science:
- Statistics
- Psychometrics
- Data Analysis
Background:
- Mediation analysis is crucial for understanding indirect effects in statistical models.
- Existing methods for constructing confidence intervals (CIs) for mediated effects have limitations.
- The PRODCLIN program, while cited, is cumbersome and has case limitations.
Purpose of the Study:
- To introduce the RMediation package for calculating confidence intervals for mediated effects.
- To offer a more robust and user-friendly alternative to existing software.
- To implement and compare various methods for CI estimation in mediation analysis.
Main Methods:
- The RMediation package utilizes the distribution-of-the-product method, Monte Carlo simulations, and asymptotic normal distribution.
- It generates percentiles, quantiles, and plots for visualizing the distribution and CI of the mediated effect.
- The package is built using the widely available R software environment.
Main Results:
- The distribution-of-the-product method demonstrates superior statistical performance for CI construction.
- RMediation provides accurate results, addressing limitations encountered with PRODCLIN.
- The package offers enhanced capabilities beyond those available in PRODCLIN.
Conclusions:
- RMediation is a powerful and accessible tool for researchers conducting mediation analysis.
- It improves the accuracy and ease of calculating confidence intervals for mediated effects.
- The package facilitates a deeper understanding of indirect effects in statistical modeling.
Related Concept Videos
Interpretation of Confidence Intervals
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Confidence Intervals
A confidence...
Uncertainty: Confidence Intervals
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Methods of Medium Optimization
Confidence Interval for Estimating Population Mean
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...

