Skip to contents

Generate Parallel Mediation Model

Source: R/GenerateModelP.R
GenerateModelP.Rd

Dynamically generates a structural equation modeling (SEM) syntax for parallel mediation analysis based on the prepared dataset. The function computes regression equations for mediators and the outcome variable, indirect effects, total effects, contrasts between indirect effect, and .

Usage

GenerateModelP(prepared_data, MP = character(0))

Arguments

prepared_data

A data frame returned by PrepareData(), containing the processed within-subject mediator and outcome variables. The data frame must include columns for difference scores (Mdiff) and average scores (Mavg) of mediators, as well as the outcome difference score (Ydiff).

MP

A character vector specifying which paths are moderated by variable(s) W. Valid values include: - "a1", "a2", ...: moderation on the a paths (W → Mdiff). - "b1", "b2", ...: moderation on the b paths (Mdiff × W → Ydiff). - "d1", "d2", ...: moderation on the d paths (Mavg × W → Ydiff). - "cp": moderation on the direct effect from X to Y (i.e., W → Ydiff).

      This argument controls which interaction terms (e.g., \code{int_Mdiff_W}, \code{int_Mavg_W}) are

      added to the corresponding regression equations.

Value

A character string representing the SEM model syntax for the specified parallel mediation analysis.

Details

This function is used to construct SEM models for parallel mediation analysis. It automatically parses variable names from the prepared dataset and dynamically creates the necessary model syntax, including:

  • Outcome regression: Defines the relationship between the difference scores of the outcome (Ydiff) and the mediators (Mdiff) as well as their average scores (Mavg).

  • Mediator regressions: Defines the intercept models for each mediator's difference score.

  • Indirect effects: Computes the indirect effects for each mediator using the product of path coefficients (e.g., a * b).

  • Total indirect effect: Calculates the sum of all indirect effects.

  • Total effect: Combines the direct effect (cp) and the total indirect effect.

  • Contrasts of indirect effects: Optionally calculates the pairwise contrasts between the indirect effects when multiple mediators are present.

  • coefficients in different 'X' conditions: Calculates path coefficients in different X conditions to observe the moderation effect of ‘X'.

This model is suitable for parallel mediation designs where multiple mediators act independently.

See also

PrepareData(), wsMed(), GenerateModelCN()

Examples

# Example prepared data
prepared_data <- data.frame(
  M1diff = rnorm(100),
  M2diff = rnorm(100),
  M1avg = rnorm(100),
  M2avg = rnorm(100),
  Ydiff = rnorm(100)
)

# Generate SEM model syntax
sem_model <- GenerateModelP(prepared_data)
cat(sem_model)
#> Ydiff ~ cp*1 + b1*M1diff + d1*M1avg + b2*M2diff + d2*M2avg
#> M1diff ~ a1*1
#> M2diff ~ a2*1
#> indirect_1 := a1 * b1
#> indirect_2 := a2 * b2
#> total_indirect := indirect_1 + indirect_2
#> total_effect := cp + total_indirect