Package 'medmod'

Title: Simple Mediation and Moderation Analysis
Description: This toolbox allows you to do simple mediation and moderation analysis. Models are estimated with the 'lavaan' package by Rosseel (2012) <doi:10.18637/jss.v048.i02>; standard errors for the mediation estimates are computed with the delta method following Sobel (1982) <doi:10.2307/270723> or by bootstrapping. It is also available as a module for 'jamovi' (see <https://www.jamovi.org> for more information). You can find an in depth tutorial on the 'lavaan' model syntax used for this package on <https://lavaan.ugent.be/tutorial/index.html>.
Authors: Ravi Selker [aut, cre]
Maintainer: Ravi Selker <[email protected]>
License: GPL (>= 2)
Version: 1.2.0
Built: 2026-07-16 08:49:59 UTC
Source: https://github.com/raviselker/medmod

Help Index

medmod: Simple Mediation and Moderation Analysis

Description

This toolbox allows you to do simple mediation and moderation analysis. Models are estimated with the 'lavaan' package by Rosseel (2012) doi:10.18637/jss.v048.i02; standard errors for the mediation estimates are computed with the delta method following Sobel (1982) doi:10.2307/270723 or by bootstrapping. It is also available as a module for 'jamovi' (see https://www.jamovi.org for more information). You can find an in depth tutorial on the 'lavaan' model syntax used for this package on https://lavaan.ugent.be/tutorial/index.html.

Details

Simple mediation analysis med()
Simple moderation analysis mod()

Author(s)

Maintainer: Ravi Selker [email protected]

Authors:

See Also

Useful links:

Mediation

Description

Simple mediation analysis. Estimates the indirect, direct and total effects of a model in which a predictor influences the dependent variable through a mediator. The model is fitted with the lavaan package (Rosseel, 2012); standard errors are computed with the delta method (equivalent to the Sobel test for the indirect effect) or by bootstrapping. Optionally provides the individual path estimates, an estimate plot, and an annotated path diagram of the model.

Usage

med(
  data,
  dep,
  med,
  pred,
  estMethod = "standard",
  bootstrap = 1000,
  test = TRUE,
  ci = FALSE,
  ciWidth = 95,
  pm = FALSE,
  paths = FALSE,
  label = FALSE,
  estPlot = FALSE,
  pathDiagram = FALSE,
  pathDiagramLabel = TRUE,
  pathDiagramEst = TRUE,
  pathDiagramSig = TRUE
)

Arguments

data

the data as a data frame
dep

a string naming the dependent variable
med

a string naming the mediator variable
pred

a string naming the predictor variable
estMethod

'standard' (default), or 'bootstrap', the estimation method to use
bootstrap

a number between 1 and 100000 (default: 1000) specifying the number of samples that need to been drawn in the bootstrap method
test

TRUE (default) or FALSE, provide 'Z' and 'p' values for the mediation estimates
ci

TRUE or FALSE (default), provide a confidence interval for the mediation estimates
ciWidth

a number between 50 and 99.9 (default: 95) specifying the confidence interval width that is used as 'ci'
pm

TRUE or FALSE (default), provide the percent mediation effect size for the mediation estimates
paths

TRUE or FALSE (default), provide the individual estimates of the paths in the mediation model
label

TRUE (default) or FALSE, provide insightful labels for all estimates
estPlot

TRUE or FALSE (default), provide an estimate plot where for each estimator the estimated coefficient and confidence intervals are plotted.
pathDiagram

TRUE or FALSE (default), provide a path diagram of the mediation model.
pathDiagramLabel

TRUE (default) or FALSE, annotate the path diagram arrows with the path labels.
pathDiagramEst

TRUE (default) or FALSE, annotate the path diagram arrows with the estimated coefficients.
pathDiagramSig

TRUE (default) or FALSE, annotate the path diagram arrows with significance stars.

Value

A results object containing:

results$med a table containing mediation estimates
results$paths a table containing the individual path estimates
results$pathDiagram an image
results$estPlot an image
results$modelSyntax the lavaan syntax used to fit the mediation model

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$med$asDF

as.data.frame(results$med)

Examples

set.seed(1234)
X <- rnorm(100)
M <- 0.5*X + rnorm(100)
Y <- 0.7*M + rnorm(100)
dat <- data.frame(X=X, M=M, Y=Y)

med(dat, dep = "Y", pred = "X", med = "M")

#
#  Mediation Estimates
#  -----------------------------------------------------
#    Effect      Estimate    SE        Z        p
#  -----------------------------------------------------
#    Indirect      0.3736    0.0920    4.059    < .001
#    Direct        0.0364    0.1044    0.348     0.728
#    Total         0.4100    0.1247    3.287     0.001
#  -----------------------------------------------------
#
#

Moderation

Description

Simple moderation analysis. Estimates a model in which the effect of a predictor on the dependent variable depends on the value of a moderator, testing the main effects of predictor and moderator and their interaction. All variables are mean-centered before the model is fitted with the lavaan package (Rosseel, 2012); standard errors are computed with the delta method or by bootstrapping. Optionally provides a simple slope analysis with a corresponding plot, and an annotated path diagram of the model.

Usage

mod(
  data,
  dep,
  mod,
  pred,
  estMethod = "standard",
  bootstrap = 1000,
  label = FALSE,
  test = TRUE,
  ci = FALSE,
  ciWidth = 95,
  pathDiagram = FALSE,
  pathDiagramLabel = TRUE,
  pathDiagramEst = TRUE,
  pathDiagramSig = TRUE,
  pathDiagramMainEffect = FALSE,
  simpleSlopeEst = FALSE,
  simpleSlopePlot = FALSE
)

Arguments

data

the data as a data frame
dep

a string naming the dependent variable
mod

a string naming the moderator variable
pred

a string naming the predictor variable
estMethod

'standard' (default), or 'bootstrap', the estimation method to use
bootstrap

a number between 1 and 100000 (default: 1000) specifying the number of samples that need to been drawn in the bootstrap method
label

TRUE or FALSE (default), provide the coefficient labels (b1, b2, b3) used in the model and the path diagram
test

TRUE (default) or FALSE, provide 'Z' and 'p' values for the mediation estimates
ci

TRUE or FALSE (default), provide a confidence interval for the mediation estimates
ciWidth

a number between 50 and 99.9 (default: 95) specifying the confidence interval width that is used as 'ci'
pathDiagram

TRUE or FALSE (default), provide a conceptual path diagram of the moderation model.
pathDiagramLabel

TRUE (default) or FALSE, annotate the path diagram arrows with the path labels.
pathDiagramEst

TRUE (default) or FALSE, annotate the path diagram arrows with the estimated coefficients.
pathDiagramSig

TRUE (default) or FALSE, annotate the path diagram arrows with significance stars.
pathDiagramMainEffect

TRUE or FALSE (default), draw the moderator's main effect on the dependent variable (b2) in the path diagram.
simpleSlopeEst

TRUE or FALSE (default), provide the estimates of the simple slopes.
simpleSlopePlot

TRUE or FALSE (default), provide a plot of the simple slopes.

Value

A results object containing:

results$mod a table containing moderation estimates
results$pathDiagram an image
results$simpleSlope$estimates a table containing the simple slope estimates
results$simpleSlope$plot an image
results$modelSyntax the lavaan syntax used to fit the moderation model

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$mod$asDF

as.data.frame(results$mod)

Examples

set.seed(1234)
X <- rnorm(100)
M <- rnorm(100)
X_M <- X*M
Y <- 0.7*X + 0.1*M + 4.2*X_M + rnorm(100)
dat <- data.frame(X=X, M=M, Y=Y)

mod(dat, dep = "Y", pred = "X", mod = "M")

#
#  Moderation Estimates
#  --------------------------------------------------
#             Estimate    SE        Z        p
#  --------------------------------------------------
#    X           0.951    0.0965     9.86    < .001
#    M          -0.471    0.0923    -5.10    < .001
#    X:M         4.185    0.1009    41.50    < .001
#  --------------------------------------------------
#
#