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.

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")

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)

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

   parallel within-subject mediation model

serial within-subject mediation model

serial-parallel within-subject mediation model

parallel-serial within-subject mediation model

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.