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wsMed

The wsMed function is designed for two condition within-subject mediation analysis, incorporating SEM models through the lavaan package and Monte Carlo simulation methods. This document provides a detailed description of the function’s parameters, workflow, and usage, along with an example demonstration.

Installation

You can install the development version of wsMed from GitHub with:

# install.packages("pak")
pak::pak("Yangzhen1999/wsMed")

Alternatively, if you prefer using devtools, you can install wsMed as follows:

# install.packages("devtools")
devtools::install_github("Yangzhen1999/wsMed")

Example

This is a basic example which shows you how to solve a common problem:

library(wsMed)

# Load example data
data(example_data)
set.seed(123)
example_dataN <- mice::ampute(
data = example_data,
prop = 0.1,
)$amp

# Perform within-subject mediation analysis (Parallel mediation model)
result <- wsMed(
data = example_dataN,   #dataset
M_C1 = c("A1","B1"),    # A1/B1 is A/B mediator variable in condition 1
M_C2 = c("A2","B2"),    # A2/B2 is A/B mediator variable in condition 2
Y_C1 = "C1",            # C1 is outcome variable in condition 1
Y_C2 = "C2",            # C2 is outcome variable in condition 2
form = "P",             # Parallel mediation
C_C1 = "D1",            # within-subject covariate (e.g., measured under D1)
C_C2 = "D2",            # within-subject covariate (e.g., measured under C2)
C = "D3",               # between-subject covariates
Na = "MI",              # Use multiple imputation for missing data
standardized = TRUE,    # Request standardized path coefficients and effects
)

# Print summary results
print(result)

Main Function Overview

The wsMed() function automates the full workflow for two-condition within-subject mediation analysis. Its main steps are:

1. Validate inputs – check dataset structure, mediation model type (form), and missing-data settings.

2. Prepare data – compute difference scores (Mdiff, Ydiff) and centered averages (Mavg) from the two-condition variables.

3. Build the model – generate SEM syntax according to the chosen structure:

   • "P": Parallel mediation

     

   • "CN": Chained / serial mediation

     

   • "CP": Chained + Parallel

     

   • "PC": Parallel + Chained

     

4. Fit the model – estimate parameters while handling missing data:

   • "DE": listwise deletion
   • "FIML": full-information ML
   • "MI": multiple imputation

5. Compute inference – provide confidence intervals using:

   • Bootstrap (ci_method = "bootstrap")
   • Monte Carlo (ci_method = "mc")

6. Optional: Standardization – if standardized = TRUE, return standardized effects with CIs.

7. Optional: Covariates – automatically center and include:

   • Between-subject covariates (C): mean-centered and added to all regressions.
   • Within-subject covariates (C_C1, C_C2): difference scores and centered averages are computed and included.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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