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Generate Combined Parallel and Chained Mediation Model

Source: R/GenerateModelCP.R
GenerateModelCP.Rd

Dynamically generates a structural equation modeling (SEM) syntax for combined parallel and chained mediation analysis based on the prepared dataset. The function computes regression equations for mediators and the outcome variable, indirect effects for both parallel and chained mediation paths, total effects, contrasts between indirect effects, and coefficients in different X conditions.

Usage

GenerateModelCP(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. Acceptable values include: - "a2", "a3", ...: moderation on the a paths of parallel mediators (W → Mdiff). - "b2", "b3", ...: moderation on the b paths of parallel mediators (Mdiff × W → Ydiff). - "b_1_2", "b_1_3", ...: moderation on the paths from the chain mediator to parallel mediators. - "d_1_2", "d_1_3", ...: moderation on the paths from the chain mediator’s Mavg to parallel mediators. - "cp": moderation on the direct effect from X to Y (i.e., W → Ydiff).

      Each entry triggers inclusion of W’s main effect or interaction terms (e.g., \code{int_Mdiff_W}).

Value

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

Details

This function is used to construct SEM models that combine parallel and chained 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), the chained mediator (M1diff), and the parallel mediators (M2diff, M3diff, etc.).

  • Mediator regressions: Defines the sequential regression models for the chained mediator and each parallel mediator.

  • Indirect effects: Computes the indirect effects for both chained and parallel mediation paths, including multi-step indirect effects involving both chained and parallel mediators.

  • Total indirect effect: Calculates the sum of all indirect effects from chained and parallel mediation paths.

  • 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 for different mediation paths.

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

This model is suitable for designs where mediators include both a sequential chain (chained mediation) and independent parallel mediators.

See also

PrepareData(), wsMed(), GenerateModelP(), GenerateModelCN()

Examples

# Example prepared data
prepared_data <- data.frame(
  M1diff = rnorm(100),
  M2diff = rnorm(100),
  M3diff = rnorm(100),
  M1avg = rnorm(100),
  M2avg = rnorm(100),
  M3avg = rnorm(100),
  Ydiff = rnorm(100)
)

# Generate SEM model syntax
sem_model <- GenerateModelCP(prepared_data)
cat(sem_model)
#> Ydiff ~ cp*1 + b1*M1diff + d1*M1avg + b2*M2diff + d2*M2avg + b3*M3diff + d3*M3avg
#> M1diff ~ a1*1
#> M2diff ~ a2*1 + b_1_2*M1diff + d_1_2*M1avg
#> M3diff ~ a3*1 + b_1_3*M1diff + d_1_3*M1avg
#> indirect_1 := a1 * b1
#> indirect_2 := a2 * b2
#> indirect_1_2 := a1 * b_1_2 * b2
#> indirect_3 := a3 * b3
#> indirect_1_3 := a1 * b_1_3 * b3
#> total_indirect := indirect_1 + indirect_2 + indirect_1_2 + indirect_3 + indirect_1_3
#> total_effect := cp + total_indirect