Package {wsMed}

Title:	Within-Subject Mediation Analysis Using Structural Equation Modeling
Version:	1.2.0
Description:	Within-subject mediation analysis using structural equation modeling. Examine how changes in an outcome variable between two conditions are mediated through one or more variables. Supports within-subject mediation analysis using the 'lavaan' package by Rosseel (2012) <doi:10.18637/jss.v048.i02>, and extends Monte Carlo confidence interval estimation to missing data scenarios using the 'semmcci' package by Pesigan and Cheung (2023) <doi:10.3758/s13428-023-02114-4>.
License:	GPL (≥ 3)
Depends:	R (≥ 4.1.0)
Encoding:	UTF-8
RoxygenNote:	7.3.2
Suggests:	rmarkdown, devtools, testthat, smcfcs (≥ 1.8.0)
Config/testthat/edition:	3
Imports:	knitr, lavaan, semmcci, mice, semboottools, MASS, rlang, dplyr, ggplot2, methods, stats, graphics, utils
VignetteBuilder:	knitr
LazyData:	true
URL:	https://yangzhen1999.github.io/wsMed/
BugReports:	https://github.com/Yangzhen1999/wsMed/issues
NeedsCompilation:	no
Maintainer:	Wendie Yang <1581075494q@gmail.com>
Packaged:	2026-10-28 02:23:46 UTC; CodexSandboxOffline
Author:	Wendie Yang [aut, cre], Shu Fai Cheung [aut]
Repository:	CRAN
Date/Publication:	2026-10-28 08:20:02 UTC

Clean all CI column names into the standard form

Description

Clean all CI column names into the standard form

Usage

.clean_ci_names(df, ci_level = 0.95)

Fit SEM and run Monte-Carlo draws

Description

Fit SEM and run Monte-Carlo draws

Usage

.fit_and_mc(
  sem_model,
  data,
  Na = c("DE", "FIML"),
  R = 20000,
  alpha = 0.05,
  fixed.x = FALSE,
  verbose = TRUE,
  run_mc = TRUE,
  seed = NULL
)

Make CI column names

Description

Make CI column names

Usage

.make_ci_names(ci)

Create moderation output for wsMed

Description

Create moderation output for wsMed

Usage

.make_moderation(
  mc_res,
  data,
  W = NULL,
  MP = NULL,
  W_type = c("categorical", "continuous", "none"),
  alpha = 0.05,
  verbose = FALSE,
  point_estimates = NULL
)

Verbose message wrapper (internal)

Description

Verbose message wrapper (internal)

Usage

.v(..., verbose = TRUE)

Average relative increase in variance

Description

Average relative increase in variance

Usage

ARIVwrapper(between, within, M, k)

Arguments

Details

The average relative increase in variance is given by

\mathrm{ARIV} = \left( 1 + M^{-1} \right) \mathrm{tr} \left( \mathbf{V}_{\mathrm{between}} \mathbf{V}_{\mathrm{within}}^{-1} \right)

Value

Returns a numeric vector of length one.

Author(s)

Ivan Jacob Agaloos Pesigan

Generate a serial mediation model

Description

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

Usage

GenerateModelCN(prepared_data, MP = character(0))

Arguments

      The function detects and inserts the correct interaction terms (e.g., \code{int_M2diff_W1}) based on these labels.

Details

This function is used to construct SEM models for 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) and the mediators (Mdiff) as well as their average scores (Mavg).

• Mediator regressions: Defines the sequential regression models for each mediator's difference score, incorporating prior mediators as predictors.

• Indirect effects: Computes the indirect effects along all possible multi-step mediation paths using the product of path coefficients.

• Total indirect effect: Calculates the sum of all indirect effects from the chained 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 chained mediation designs where mediators influence each other in a sequential manner, forming multi-step mediation paths.

Value

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

Product moments

When a moderated path starts at an endogenous mediator, auxiliary product moment equations and covariances with its upstream disturbances preserve the joint predictor moment structure under reference recoding. These equations are not additional mediation paths. Genuine exogenous moments follow the fitting function's fixed.x setting; endogenous product moments are estimated.

See Also

PrepareData(), wsMed(), GenerateModelP()

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 <- GenerateModelCN(prepared_data)
cat(sem_model)

Generate a serial-parallel mediation model

Description

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

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

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.

Value

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

Product moments

When a moderated path starts at an endogenous mediator, auxiliary product moment equations and covariances with its upstream disturbances preserve the joint predictor moment structure under reference recoding. These equations are not additional mediation paths. Genuine exogenous moments follow the fitting function's fixed.x setting; endogenous product moments are estimated.

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)

Generate a user-defined mediation model

Description

Generates lavaan model syntax for a user-defined within-subject mediation model. Users specify directed paths among mediators and from mediators to the outcome. The function automatically adds the corresponding difference and level components and identifies all indirect effects.

Usage

GenerateModelCustom(prepared_data, paths, MP = character(0))

Arguments

Value

A character string containing lavaan model syntax.

Product moments

When a moderated path starts at an endogenous mediator, auxiliary product moment equations and covariances with its upstream disturbances preserve the joint predictor moment structure under reference recoding. These equations are not additional mediation paths. Genuine exogenous moments follow the fitting function's fixed.x setting; endogenous product moments are estimated.

Examples

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

model <- GenerateModelCustom(
 prepared_data = prepared_data,
 paths = c(
   "M1 -> M3",
   "M1 -> Y",
   "M3 -> Y",
   "M2 -> Y"
 )
)

cat(model)

Generate a parallel mediation model

Description

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

      This argument controls which interaction terms (e.g., \code{int_Mdiff_W}, \code{int_Mavg_W}) are
      added to the corresponding regression equations.

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.

Value

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

Product moments

When a moderated path starts at an endogenous mediator, auxiliary product moment equations and covariances with its upstream disturbances preserve the joint predictor moment structure under reference recoding. These equations are not additional mediation paths. Genuine exogenous moments follow the fitting function's fixed.x setting; endogenous product moments are estimated.

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)

Generate a parallel-serial mediation model

Description

Dynamically generates a structural equation modeling (SEM) syntax for mediation analysis that integrates both parallel and chained mediators. Unlike the Combined Parallel and Chained mediation model (GenerateModelCP), this function assumes that the chained mediator receives inputs from the parallel mediators and directly influences the outcome variable, emphasizing a downstream role for the chained mediator.

Usage

GenerateModelPC(prepared_data, MP = character(0))

Arguments

      This argument controls which interaction terms (e.g., \code{int_Mdiff_W}, \code{int_Mavg_W}) are included
      in the regression equations. Variable names are automatically matched using the naming convention
      \code{"int_<predictor>_W<index>"}.

Details

This function is designed to build SEM models that integrate parallel and chained mediation structures. It automatically identifies variable names from the prepared dataset and generates 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: Constructs separate regression models for the parallel mediators and the chained mediator. The chained mediator incorporates predictors from all parallel mediators.

• Indirect effects: Computes indirect effects for:

  • Parallel mediators (M2diff, M3diff, etc.) directly influencing the outcome.

  • The chained mediator (M1diff) directly influencing the outcome.

  • Combined paths where parallel mediators influence the chained mediator, which in turn influences the outcome.

• Total indirect effect: Summarizes all indirect effects from parallel and chained mediation paths.

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

• Contrasts of indirect effects: Optionally computes pairwise contrasts between indirect effects for different mediation paths.

• Coefficients in different 'X' conditions: Computes path coefficients under different X conditions to analyze moderation effects.

This model is suitable for designs where mediators include both independent parallel paths and sequential chained paths, providing a comprehensive mediation analysis framework.

Value

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

Product moments

When a moderated path starts at an endogenous mediator, auxiliary product moment equations and covariances with its upstream disturbances preserve the joint predictor moment structure under reference recoding. These equations are not additional mediation paths. Genuine exogenous moments follow the fitting function's fixed.x setting; endogenous product moments are estimated.

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 <- GenerateModelPC(prepared_data)
cat(sem_model)

Impute missing data using multiple imputation

Description

The ImputeData function performs multiple imputation on a data frame with missing values using the mice package. It handles missing data by creating multiple imputed datasets based on a specified imputation method and returns a list of completed data frames.

Usage

ImputeData(
  data_missing,
  m = 5,
  method = "pmm",
  seed = 123,
  predictorMatrix = NULL
)

Arguments

Details

This function replaces specified missing value placeholders (e.g., -999) with NA, and then applies the multiple imputation by chained equations (MICE) procedure to generate multiple imputed datasets. It supports flexible imputation methods and allows for specifying a custom predictor matrix.

Value

A list of m imputed data frames.

Author(s)

Wendie Yang, Shufai Cheung

Examples

# Example data with missing values
data <- data.frame(
  M1 = c(rnorm(99), rep(NA, 1)),
  M2 = c(rnorm(99), rep(NA, 1)),
  Y1 = rnorm(100),
  Y2 = rnorm(100)
)
# Perform multiple imputation
imputed_data_list <- ImputeData(data, m = 5)
# Display the first imputed dataset
head(imputed_data_list[[1]])

Convert lavaan model to RAM matrices

Description

Converts a lavaan-style matrix list into RAM (Reticular Action Model) format, including the A (asymmetric paths), S (symmetric paths), F (filter matrix), and M (means/intercepts) matrices.

Usage

Lav2RAM2(lav_mod)

Arguments

Details

This function reorganizes the lavaan-style matrices into the RAM representation, commonly used for model standardization and transformation.

Value

A list with components:

A

Asymmetric path matrix (including factor loadings and structural paths)

S

Symmetric path matrix (variances and covariances)

F

Filter matrix mapping latent and observed variables

M

Row vector of intercepts/means

Process Monte Carlo samples for defined parameters in SEM

Description

A wrapper for the internal .MCDef() function from the semmcci package. This function processes Monte Carlo samples to compute defined parameters for structural equation modeling (SEM).

Usage

MCDefWrapper(object, thetahat, thetahatstar_orig)

Arguments

Details

This function takes Monte Carlo samples of parameter estimates and a fitted SEM model object to compute the defined parameters (e.g., indirect effects or user-defined contrasts) based on the model syntax. It is particularly useful for examining derived quantities in SEM analyses using Monte Carlo methods.

Value

A matrix of computed defined parameters for each Monte Carlo sample.

See Also

MCMI2(), RunMCMIAnalysis()

Monte Carlo confidence intervals for multiple imputation SEM models

Description

Computes Monte Carlo confidence intervals (MCCI) for structural equation models (SEM) fitted to multiple imputed datasets. This function integrates SEM fitting across imputed datasets, pools the results, and generates confidence intervals through Monte Carlo sampling.

Usage

MCMI2(
  sem_model,
  imputations,
  R = 20000L,
  alpha = c(0.001, 0.01, 0.05),
  decomposition = "eigen",
  pd = TRUE,
  tol = 1e-06,
  seed = NULL,
  estimator = "ML",
  se = "standard",
  missing = "listwise",
  fixed.x = FALSE
)

Arguments

Details

This function is designed for SEM models that require multiple imputation to handle missing data. It performs the following steps:

• SEM Fitting: Fits the specified SEM model to each imputed dataset using lavaan::sem().

• Pooling Results: Combines parameter estimates and covariance matrices across imputations using Rubin's rules.

• Monte Carlo Sampling: Generates Monte Carlo samples based on the pooled estimates and covariance matrices, and calculates confidence intervals for model parameters.

This function supports custom estimators, handling of missing data, and precision adjustments for Monte Carlo sampling. It is particularly useful for mediation analysis or complex SEM models where missing data are addressed using multiple imputation.

With decomposition = "eigen", Monte Carlo sampling uses the principal symmetric covariance square root. Equivalent eigenvector signs and repeated-eigenvalue bases therefore produce the same draws up to numerical precision. Fixed-seed draws differ from wsMed 1.1.0; the target distribution and pooled point estimates are unchanged. Reproducing the complete analysis also requires matching data, imputation settings, and software dependencies.

Value

An object of class semmcci containing:

• call: The matched function call.

• args: A list of input arguments.

• thetahat: The pooled parameter estimates.

• thetahatstar: Monte Carlo samples for parameter estimates.

• fun: The name of the function ("MCMI2").

See Also

lavaan::sem(), semmcci::MC(), semmcci::MCStd()

Examples

# Example SEM model
sem_model <- "
  Ydiff ~ b1 * M1diff + cp * 1
  M1diff ~ a1 * 1
  indirect := a1 * b1
  total := cp + indirect
"

# Example imputed datasets
imputations <- list(
  data.frame(M1diff = rnorm(100), Ydiff = rnorm(100)),
  data.frame(M1diff = rnorm(100), Ydiff = rnorm(100))
)

# Compute Monte Carlo confidence intervals
result <- MCMI2(
  sem_model = sem_model,
  imputations = imputations,
  R = 1000,
  alpha = c(0.05, 0.01),
  seed = 123
)

Monte Carlo summary for standardized estimates

Description

Computes standardized estimates, standard errors, and confidence intervals based on Monte Carlo samples from a semmcci object. This function fully standardizes both point estimates and sampling distributions (including intercepts).

Usage

MCStd2(mc, alpha = c(0.001, 0.01, 0.05))

Arguments

Details

The function standardizes the sampling distribution using StdLav2() on each Monte Carlo draw, then summarizes the distribution into SEs and quantile-based confidence intervals. Invalid draws are excluded jointly and reported in the standardization_diagnostics attribute (requested, valid, invalid, indices and reasons).

Value

A data frame containing:

Parameter

Parameter name

Estimate

Standardized point estimate

SE

Standard deviation of standardized samples

R

Number of valid joint Monte Carlo replications

CI columns

Multiple confidence intervals based on alpha

Wrapper for internal multiple imputation combining function

Description

A wrapper function for the internal .MICombine() function from the semmcci package. This function pools parameter estimates and covariance matrices from multiple imputed datasets using Rubin's rules.

Usage

MICombineWrapper(coefs, vcovs, M, k, adj = FALSE)

Arguments

Details

This wrapper provides an abstraction over the internal .MICombine() function from the semmcci package, allowing it to be used within your package without directly exposing the internal function. It is primarily designed for use within workflows that involve multiple imputation and structural equation modeling (SEM). Rubin's rules are applied to compute pooled parameter estimates and their covariance matrices.

Value

A list containing the pooled estimates and covariance matrix:

• est: A numeric vector of pooled parameter estimates.

• total: A numeric matrix representing the pooled covariance matrix.

See Also

RunMCMIAnalysis()

Prepare data for two-condition mediation

Description

PrepareData() transforms raw pre/post data into the set of variables required by the wsMed workflow. It handles mediators, outcome, within-subject controls, between-subject controls, moderators, and all necessary interaction terms, while automatically centering / dummy-coding variables as needed.

Usage

PrepareData(
  data,
  M_C1,
  M_C2,
  Y_C1,
  Y_C2,
  C_C1 = NULL,
  C_C2 = NULL,
  C = NULL,
  C_type = NULL,
  W = NULL,
  W_type = NULL,
  center_W = TRUE,
  keep_W_raw = TRUE,
  keep_C_raw = TRUE
)

Arguments

Details

The function performs the following steps:

1. Outcome difference: Ydiff = Y_C2 - Y_C1.

2. Mediator variables for each pair (M_C1[i], M_C2[i]):

   • Mi_diff = M_C2 - M_C1

   • Mi_avg is the mean-centered average of the two occasions.

3. Between-subject controls C:

   • Continuous variables are grand-mean centered (Cb1, Cb2, ...).

   • Categorical variables (binary or multi-level) are expanded into k - 1 dummy variables (Cb1_1, Cb2_1, Cb2_2, ...), using the first level as the reference.

4. Within-subject controls Cw: difference and centered-average versions (Cw1diff, Cw1avg, ...).

5. Moderator W (at most one):

   • Continuous variables are grand-mean centered (W1, W2, ...).

   • Categorical variables are dummy-coded in the same way as C.

6. Interaction terms between each moderator column and each mediator column:

   • int_<Mi_diff>_<Wj>, int_<Mi_avg>_<Wj>.

7. Two attributes are added to the returned data:

   • "W_info": raw names, dummy names, level mapping

   • "C_info": same structure for between-subject controls.

Row counts are preserved even if input factors contain NA values (model.matrix is called with na.action = na.pass).

Value

A data frame containing at minimum:

• Ydiff

• Mi_diff, Mi_avg for each mediator

• centered or dummy-coded Cb*, Cw*diff, Cw*avg

• centered or dummy-coded W* and all int_* interaction terms

plus the attributes "W_info" and "C_info" described above.

See Also

PrepareMissingData, GenerateModelP, wsMed, wsmed_categorical

Examples

set.seed(1)
raw <- data.frame(
  A1 = rnorm(50), A2 = rnorm(50),   # mediator 1
  B1 = rnorm(50), B2 = rnorm(50),   # mediator 2
  C1 = rnorm(50), C2 = rnorm(50),   # outcome
  D1 = rnorm(50), D2 = rnorm(50),   # within-subject control
  W_bin  = sample(0:1, 50, TRUE),   # between-subject binary C
  W_fac3 = factor(sample(c("Low","Med","High"), 50, TRUE)) # moderator W
)

prep <- PrepareData(
  data  = raw,
  M_C1  = c("A1","B1"), M_C2 = c("A2","B2"),
  Y_C1  = "C1",         Y_C2 = "C2",
  C_C1  = "D1",         C_C2 = "D2",
  C     = "W_bin",      C_type = "categorical",
  W     = "W_fac3",     W_type = "categorical"
)
head(prep)

Prepare data with missing values for mediation analysis

Description

Handles missing values in the dataset through multiple imputation and prepares the imputed datasets for within-subject mediation analysis. The function imputes missing data, processes each imputed dataset, and provides diagnostics for the imputation process.

Usage

PrepareMissingData(
  data_missing,
  m = 5,
  method_num = "pmm",
  seed = 123,
  M_C1,
  M_C2,
  Y_C1,
  Y_C2,
  C_C1 = NULL,
  C_C2 = NULL,
  C = NULL,
  C_type = NULL,
  W = NULL,
  W_type = NULL,
  center_W = TRUE,
  keep_W_raw = TRUE,
  keep_C_raw = TRUE
)

Arguments

Details

This function is designed to preprocess datasets with missing values for mediation analysis. It performs the following steps:

• Multiple imputation: Uses specified imputation methods (for example, predictive mean matching) to generate m imputed datasets.

• Data preparation: Applies PrepareData to each of the m imputed datasets to calculate difference scores and centered averages for mediators and the outcome variable.

• Imputation diagnostics: Provides summary diagnostics for the imputation process, including information about missing data patterns and convergence.

This function integrates imputation and data preparation, ensuring that the resulting datasets are ready for subsequent mediation analysis.

Value

A list containing:

processed_data_list

A list of m data frames, each representing an imputed and processed dataset ready for within-subject mediation analysis.

imputation_summary

A summary of the imputation process, including diagnostics and convergence information.

See Also

PrepareData, ImputeData, wsMed

Examples

# Example dataset with missing values
data("example_data", package = "wsMed")
set.seed(123)
example_dataN <- mice::ampute(
  data = example_data,
  prop = 0.1
)$amp

# Prepare the dataset with multiple imputations
prepared_missing_data <- PrepareMissingData(
  data_missing = example_dataN,
  m = 5,
  M_C1 = c("A2", "B2"),
  M_C2 = c("A1", "B1"),
  Y_C1 = "C2",
  Y_C2 = "C1"
)

# Access processed datasets
processed_data_list <- prepared_missing_data$processed_data_list
imputation_summary  <- prepared_missing_data$imputation_summary

Convert standardized RAM back to lavaan matrices

Description

Converts a standardized RAM object back to lavaan-style matrix structure. Preserves residual covariances on their transformed scales.

Usage

RAM2Lav2(ram, lav_mod, standardized = FALSE)

Arguments

Details

This function restores the internal lavaan model matrix structure from a RAM representation.

Value

A modified lavaan-style matrix list with updated lambda, beta, theta, psi, and alpha.

Generate Gaussian random variates using singular value decomposition

Description

Generate Gaussian random variates using singular value decomposition

Usage

RandomGaussianSVDwrapper(Z, svd)

Arguments

Value

Numeric matrix.

Author(s)

Ivan Jacob Agaloos Pesigan

Monte Carlo SEM with multiple imputation (wsMed workflow)

Description

RunMCMIAnalysis() is a helper that:

1. imputes missing data via PrepareMissingData;

2. generates all WsMed variables in each completed data set;

3. fits the user-supplied SEM model to each replicate; and

4. pools the results via MCMI2(), producing Monte Carlo confidence intervals (MCCI) for all model parameters.

Usage

RunMCMIAnalysis(
  data_missing,
  m = 5,
  method_num = "pmm",
  seed = 123,
  M_C1,
  M_C2,
  Y_C1,
  Y_C2,
  C_C1 = NULL,
  C_C2 = NULL,
  C = NULL,
  C_type = NULL,
  W = NULL,
  W_type = NULL,
  keep_W_raw = TRUE,
  keep_C_raw = TRUE,
  sem_model,
  Na = "MI",
  R = 20000L,
  alpha = c(0.001, 0.01, 0.05),
  decomposition = "eigen",
  pd = TRUE,
  tol = 1e-06,
  fixed.x = FALSE
)

Arguments

Details

Internally the function calls:

• PrepareMissingData() – performs multiple imputation (logreg / polyreg for categorical variables, and method_num for numeric) and applies PrepareData to each imputed set;

• MCMI2() – pools parameter estimates across the m imputations and draws R Monte Carlo samples.

Only the missing-data strategy Na = "MI" is supported.

Value

A list with three elements:

mc_result

A semmcci object returned by MCMI2().

first_imputed_data

The first processed data frame (useful for inspection or plotting).

imputation_summary

Diagnostics from PrepareMissingData().

See Also

PrepareMissingData, PrepareData, MCMI2, wsMed

Standardize parameter estimates in a lavaan model

Description

Applies full standardization (including intercepts) to a fitted lavaan model by converting to RAM form, performing standardization, and converting back to lavaan matrix structure.

Usage

StdLav2(
  est,
  object,
  marginal_variances = object@external$wsmed_mi_marginal$point
)

Arguments

Details

The function extracts the model's RAM representation via Lav2RAM2, applies StdRAM2 standardization, restores the standardized GLIST via RAM2Lav2, and retrieves standardized user-defined parameter estimates with lav_model_get_parameters().

Value

A numeric vector of fully standardized parameter estimates (including intercepts and defined parameters).

Standardize RAM matrices

Description

Performs standardization of RAM matrices by rescaling path and variance structures using the implied covariance matrix. Intercepts are also standardized.

Usage

StdRAM2(ram_est, roles = NULL, marginal_variances = NULL)

Arguments

Details

The function computes the implied covariance matrix \Sigma = (I - A)^{-1} S (I - A)^{-T}, extracts standard deviations, and performs standardization via D^{-1} scaling.

Value

A list of standardized RAM matrices:

A

Standardized asymmetric path matrix

S

Standardized symmetric path matrix

F

Unchanged filter matrix

M

Standardized intercept vector

Test for a positive definite matrix

Description

Returns TRUE if input is a positive definite matrix, and FALSE otherwise.

Usage

TestPositiveDefinitewrapper(eigen, tol = 1e-06)

Arguments

Details

A k \times k symmetric matrix \mathbf{A} is positive definite if all of its eigenvalues are positive.

Value

Logical.

References

Wikipedia: Definite matrix

Monte Carlo sampling for parameter estimates

Description

Generates Monte Carlo samples for parameter estimates using a covariance matrix and a location vector. With eigen decomposition, sampling uses the principal symmetric square root of the covariance matrix.

Usage

ThetaHatStarWrapper(
  R = 20000L,
  scale,
  location,
  decomposition = "eigen",
  pd = TRUE,
  tol = 1e-06
)

Arguments

Details

The eigen sampler is invariant to eigenvector signs and to rotations within eigenspaces with repeated eigenvalues, up to numerical precision. This changes fixed-seed draws relative to wsMed 1.1.0, while preserving their target distribution. It does not guarantee identical results across different fitted inputs, imputation algorithms, or software environments.

Value

A list containing:

• thetahatstar: A matrix of simulated parameter estimates with dimensions ⁠R x length(location)⁠.

• decomposition: The decomposition method used.

See Also

MCMI2(), RunMCMIAnalysis()

Compute updated parameter estimates for SEM models

Description

A wrapper for the internal .ThetaHat() function from the semmcci package. This function computes updated parameter estimates for structural equation models (SEM) using pooled estimates from multiple imputations or Monte Carlo simulations.

Usage

ThetaHatWrapper(object, est = NULL)

Arguments

Details

The function takes a fitted SEM model object and pooled parameter estimates to calculate the updated parameter values. It is particularly useful for combining results from multiple imputations or Monte Carlo samples to refine parameter estimates.

Value

A numeric vector of updated parameter estimates.

See Also

MCMI2(), RunMCMIAnalysis()

Examples

NULL

Adjusted total sampling covariance matrix

Description

Adjusted total sampling covariance matrix

Usage

TotalAdjwrapper(ariv, within)

Arguments

Details

The adjusted total sampling covariance matrix is given by

\tilde{\mathbf{V}}_{\mathrm{total}} = \left( 1 + \mathrm{ARIV} \right) \mathbf{V}_{\mathrm{within}}

Author(s)

Ivan Jacob Agaloos Pesigan

Apply PrepareData to imputed datasets and return new mids object

Description

This function applies the PrepareData() preprocessing step to each imputed dataset in a mids object and returns a new mids object with the transformed data. It is designed for use in within-subject mediation analysis pipelines when working with multiply imputed data.

Usage

TransformMidsWithPrepareData(mids_obj, M_C1, M_C2, Y_C1, Y_C2)

Arguments

Details

The function performs the following steps:

• Extracts all imputed datasets from the original mids object.

• Applies PrepareData() to each imputed dataset using the specified within-subject mediator and outcome variables.

• Combines all processed datasets into a long format and reconstructs a new mids object via mice::as.mids().

This enables subsequent analyses to operate on a version of the multiply imputed data where difference scores and averages have already been computed.

Value

A new mids object where each imputed dataset has been processed using PrepareData().

Append significance stars based on CI

Description

Append significance stars based on CI

Usage

add_sig(df)

Summarize effects with a continuous moderator

Description

analyze_mm_continuous() summarises Monte Carlo draws from a semmcci object when the moderator W is continuous. It reports moderated path coefficients, conditional indirect effects, conditional path coefficients, and effect curves across values of the moderator.

Usage

analyze_mm_continuous(
  mc_result,
  data,
  MP,
  W_raw_name = "W",
  ci_level = 0.95,
  W_values = NULL,
  n_curve = 120,
  digits = 8,
  point_estimates = NULL
)

Arguments

Details

The function first summarises the coefficients associated with moderated paths, including interaction terms such as aw, bw, dw, and cpw, together with their corresponding base coefficients and confidence intervals.

It then evaluates conditional indirect effects and moderated path coefficients at three reference values of the moderator. By default, these values are the moderator mean and one standard deviation below and above the mean. Alternative reference values can be supplied through W_values.

Finally, the function evaluates the conditional indirect effects and moderated path coefficients over a moderator grid. These results can be used to plot effect curves or identify regions in which the confidence interval excludes zero.

An asterisk is added to an effect when its confidence interval excludes zero.

Value

A named list with the following components:

mod_coeff

A data frame summarising the moderated path coefficients, their corresponding base coefficients, and confidence intervals.

beta_coef

A data frame containing the conditional indirect effects at the three reference values of the moderator.

path_HML

A data frame containing the conditional moderated path coefficients at the three reference values of the moderator.

theta_curve

A data frame containing the conditional indirect effects evaluated over the moderator grid.

path_curve

A data frame containing the conditional path coefficients evaluated over the moderator grid.

Apply standardization to parameter definitions

Description

Replaces parameters in expressions with their standardized versions.

Usage

apply_standardization(definitions, std_map, path_std_map = list())

Arguments

Value

A list of standardized parameter expressions.

Assert a scalar (whole-number) integer with optional bounds

Description

Assert a scalar (whole-number) integer with optional bounds

Usage

assert_scalar_int(
  x,
  name = deparse(substitute(x)),
  lower = NULL,
  upper = NULL,
  allow_null = FALSE
)

Bootstrap standard deviations for standardization

Description

Performs bootstrap sampling to estimate SDs of variables used in standardization.

Usage

bootstrap_sd_list(data, var_names, nboot = 20000, seed = NULL)

Arguments

Value

A named list of numeric vectors (bootstrapped SD samples).

Build unstandardized parameter definitions

Description

Extracts parameter definition expressions from a fitted model.

Usage

build_definitions(fit)

Arguments

Value

A named list of parameter definitions.

Build path-specific standardization maps

Description

Constructs mapping of parameter labels to variables for standardization.

Usage

build_path_std_map(fit)

Arguments

Value

A named list: slopes (path_std_map).

Build standardization maps

Description

Constructs mapping of parameter labels to variables for standardization.

Usage

build_std_map(fit)

Arguments

Value

A named list: intercepts (std_map).

Basic contrasts for indirect effects and pre/post path coefficients

Description

calc_basic_contrasts() extracts two convenient sets of contrasts from a Monte-Carlo SEM result (semmcci object):

1. All pairwise differences between indirect effects (⁠indirect_*⁠ columns);

2. Pre-test (X_0) and post-test (X_1) coefficients for every primary b path, obtained with X_1 = (2b + d)/2, X_0 = X_1 - d.

Usage

calc_basic_contrasts(
  mc_result,
  ci_level = 0.95,
  digits = 3,
  point_estimates = NULL
)

Arguments

Details

• Indirect-effect columns are detected by the regular expression ⁠^indirect_⁠.

• A primary b path is any coefficient named b1, b_1_2, … Its matching d path (d1, d_1_2, …) is paired automatically.

Each contrast is summarised with its plug-in estimate, sampling SD, and a symmetric 100(1-\alpha) % confidence interval. Helper functions mc_summary_pct() and fix_pct_names() ensure that the final CI columns are named, for example, ⁠2.5%CI.Lo⁠ and ⁠97.5%CI.Up⁠.

Value

A list with up to two data frames:

IE_contrasts

Pairwise contrasts of indirect effects, or NULL if fewer than two are present.

Xcoef

Rows ⁠X1_b*⁠ and ⁠X0_b*⁠ for every detected b path, or NULL if no b path is found.

Debug printer with indentation (internal)

Description

Debug printer with indentation (internal)

Usage

dbg(..., .lvl = 0, verbose = TRUE)

Evaluate unstandardized Monte Carlo definitions

Description

Evaluates user-defined parameter expressions from Monte Carlo samples without any standardization.

Usage

evaluate_definitions_unstd_v2(theta_star, definitions)

Arguments

Value

A data frame with R rows (simulations) and p + d columns (p = number of free parameters, d = number of defined parameters).

Evaluate standardized Monte Carlo expressions

Description

Evaluates user-defined parameters (defined via expressions) and standardized free parameters from Monte Carlo simulated samples. This version supports both intercept-based standardization and path-based standardization.

Usage

evaluate_definitions_v3(
  theta_star,
  definitions,
  std_map,
  sd_boot_list,
  sd_var_boot,
  path_std_map
)

Arguments

Value

A data frame of R rows (simulations) * all parameters (standardized free + defined).

Simulated data for mediation tutorials

Description

Artificial data used to demonstrate model syntax, estimation, and categorical predictors. The labels do not identify substantive psychological constructs. This is distinct from the empirical manuscript dataset wsmed_examples().

Usage

example_data

Format

A tibble (data frame) with 100 rows and 14 variables:

A1, A2, A3

Numeric measurements for variable A at three conditions.

B1, B2, B3

Numeric measurements for variable B at three conditions.

C1, C2, C3

Numeric measurements for variable C at three conditions.

D1, D2, D3

Numeric measurements for variable D at three conditions.

Group

An unordered factor with levels high, med, and low, in that order.

W_Group

An unordered factor with levels high, med, and low, in that order.

Details

All columns are complete. A wsMed analysis selects two condition columns per outcome or mediator. The factor labels are nominal categories; they do not request an ordinal regression model. Set their level order explicitly before fitting to select a different reference category.

See Also

wsmed_examples(), wsmed_categorical

Examples

data(example_data)
dim(example_data)
levels(example_data$Group)

Extract all parameters and definitions

Description

Extracts free and defined parameters from a fitted lavaan model, and builds a dependency map of user-defined parameters.

Usage

extract_all_parameters(fit)

Arguments

Value

A list with:

• free: Names of free parameters.

• defined: Names of defined parameters.

• definition_map: A list mapping defined parameters to their dependencies.

Fix legacy CI column names in a data.frame

Description

Converts columns like CI[LL] / CI[UL] or CI.LL / CI.UL to the standard produced by .make_ci_names().

Usage

fix_ci_names(df, ci = 0.95)

Repair percent signs altered by make.names()

Description

Repair percent signs altered by make.names()

Usage

fix_pct_names(df)

Generate Monte Carlo samples

Description

Generates a Monte Carlo sample of model parameters from a multivariate normal distribution.

Usage

generate_mc_samples(fit, resolved_map, R = 20000, seed = NULL)

Arguments

Value

A numeric matrix of Monte Carlo samples.

Extract all variables needed for standardization

Description

Extracts predictor and outcome variable names from a path standardization map.

Usage

get_all_variables_from_path_map(path_std_map)

Arguments

Value

A character vector of variable names.

Parse indirect paths from coefficient names

Description

Infers every unique mediation chain that can be constructed from a set of coefficient names following the WsMed naming convention (e.g., a1, b_1_3, d_2_1, b3). The function returns a list; each element describes one indirect effect:

• path_name – canonical name, e.g.\ "indirect_effect_1_3"
  (mediators are concatenated in the encountered order);

• coefs – vector of coefficient names that define the path;

• mediators – readable label such as "M1 M3".

Usage

get_indirect_paths(col_names)

Arguments

Details

Internally the function:

1. identifies all a, b, and d coefficients available in col_names;

2. constructs a directed graph from X → Mi, Mi → Y, and Mi → Mj edges;

3. runs a depth-first search from the exposure (X) to the outcome (Y);

4. converts every node sequence (X → … → Y) into the corresponding coefficient sequence.

Duplicate paths (identical mediator ordering) are removed.

Value

A list of path descriptors; each element is itself a list with components path_name, coefs, and mediators. If no valid path exists, an empty list is returned.

Get safe number of CPUs for parallel processing

Description

Automatically returns 1 when on CI to avoid errors.

Usage

get_safe_ncpus()

Extract target variables for standardization

Description

Extracts variable names from a fitted SEM model and a definition map that are required for computing standardized estimates in Monte Carlo simulations. Specifically, it identifies intercept terms (e.g., ~1) involved in user-defined parameters such as indirect effects (e.g., indirect := a * b) that require standard deviations for standardization.

Usage

get_sd_target_variables(fit, definition_map, data)

Arguments

Value

A character vector of variable names whose standard deviations are needed to standardize the intercept estimates in Monte Carlo confidence interval analysis.

Compare indirect effects across moderator levels

Description

Compare indirect effects across moderator levels

Usage

make_contrasts(df, value_col = "Estimate", contrast_col = "Contrast")

Compute Monte Carlo estimates, standard errors, and CIs (with percent labels)

Description

mc_summary_pct() summarizes a numeric vector of Monte Carlo samples x by computing its mean, standard deviation, and percentile-based confidence interval. The result is returned as a data.frame with column names that include percentage signs. This function is typically used to generate tables of mediation effects or path coefficient estimates with path labels.

Returned columns include:

• Path: Path label.

• Estimate: Sample mean.

• SE: Sample standard deviation.

• ⁠<p%CI.Lo>⁠, ⁠<p%CI.Up>⁠: Lower and upper bounds of the percentile CI, with ⁠%⁠ in the column names.

Usage

mc_summary_pct(x, label, ci_level = 0.95, digits = 3)

Arguments

Value

A data.frame containing the path label, estimate, standard error, and CI. Column names are formatted like "2.5%CI.Lo" and "97.5%CI.Up".

Null-coalescing operator

Description

Returns x unless it is NULL, otherwise returns y.

Usage

x %||% y

Plot conditional effects at moderator levels

Description

Draw point estimates and confidence intervals by categorical group or by the stored low, mean and high levels of a continuous moderator. Each effect is shown in its own panel. Categories are not joined by interpolating lines.

Usage

plot_conditional_effects(
  result,
  paths = NULL,
  type = c("indirect", "paths", "overall"),
  levels = NULL,
  standardized = FALSE,
  engine = c("mc", "boot"),
  labels = NULL,
  title = NULL,
  x_label = NULL,
  y_label = NULL,
  base_size = 12,
  ncol = NULL
)

Arguments

Details

Intervals are the stored percentile conditional intervals, including jointly transformed scales for standardized results. They are individual intervals, not simultaneous confidence intervals.

Value

A ggplot object with plotted data and a wsmed_plot attribute.

See Also

plot_effects(), plot_contrasts()

Plot differences between mediation effects

Description

Draw contrast estimates and their confidence intervals relative to zero. With a moderator, contrasts compare groups or stored moderator levels within each effect. Without a moderator, specific indirect paths are compared with one another. The displayed subtraction order is explicit.

Usage

plot_contrasts(
  result,
  paths = NULL,
  type = c("indirect", "paths", "overall"),
  contrasts = NULL,
  standardized = FALSE,
  engine = c("mc", "boot"),
  labels = NULL,
  title = NULL,
  x_label = NULL,
  y_label = NULL,
  base_size = 12,
  ncol = NULL
)

Arguments

Details

Moderator contrasts use the existing joint-draw contrast tables. Without a moderator, only type = "indirect" is available: contrasts are computed from the stored joint draws, with point estimates evaluated at fitted/pooled parameters. Their intervals are percentile intervals. Standardized draws use the same draw-specific marginal endpoint scales as other standardized effects. No model is refitted. Interval endpoints for two separate effects are never subtracted to construct a contrast CI. paths and labels apply to effect panels when a moderator is present; without a moderator, paths selects the indirect effects to compare and labels maps the full contrast labels. Continuous overall contrasts are unavailable when the fitted result does not contain that contrast table.

Value

A ggplot object with plotted data and a wsmed_plot attribute.

See Also

plot_effects(), plot_conditional_effects()

Forest plot of mediation effects

Description

Plot fitted indirect effects, the total indirect effect, the direct effect (cp) and the total effect with their stored confidence intervals. With a moderator these are effects at the model's reference value (centered continuous W = 0, or the reference category), not averages across W. Use plot_conditional_effects() to show selected moderator levels instead.

Usage

plot_effects(
  result,
  paths = NULL,
  standardized = FALSE,
  engine = c("mc", "boot"),
  labels = NULL,
  title = NULL,
  x_label = NULL,
  y_label = NULL,
  base_size = 12
)

Arguments

Details

MC intervals come from the parameter draws (or the stored standardized parameter table). Bootstrap plots use the bootstrap limits, not the normal-theory limits that may also be present in the table. New workflow objects use wsmed_plots. Both interfaces expose full-precision estimate, conf.low, and conf.high columns in the plotted data; legacy column names remain available. Interval conventions are not silently changed.

Value

A ggplot object. Its data contains the plotted estimates and CI.LL/CI.UL limits. The wsmed_plot attribute records the selected engine, scale and confidence level. Use ggplot2::ggsave() to export it.

See Also

plot_conditional_effects(), plot_contrasts(), plot_moderation_curve()

Plot conditional effects across a continuous moderator

Description

Plot a conditional indirect effect, total effect or path coefficient with its pointwise confidence band. Highlight stored grid values whose interval excludes zero. Boundary labels use raw moderator units, not sample percentiles.

Usage

plot_moderation_curve(
  result,
  path_name,
  title = NULL,
  x_label = "Moderator (W, raw units)",
  y_label = "Estimate",
  ns_fill = "#FEE0D2",
  sig_fill = "#C7E9C0",
  alpha_ci = 0.35,
  alpha_sig = 0.35,
  base_size = 14,
  standardized = FALSE,
  engine = c("mc", "boot")
)

Arguments

Details

Highlighted intervals are approximate ranges on the stored grid, not exact Johnson–Neyman boundaries or simultaneous confidence regions. A label ⁠[a, b]⁠ reports the first and last qualifying grid values. An isolated qualifying point is marked by a vertical line. Missing confidence limits break a highlighted range. The displayed confidence level follows result$alpha; no fixed p-value threshold is implied.

Value

A ggplot object. Its data contains the selected curve; the wsmed_regions attribute contains raw-unit grid interval endpoints. The wsmed_plot attribute records engine, scale and confidence level.

See Also

plot_effects(), plot_conditional_effects(), plot_contrasts()

Print formatted SEM model syntax

Description

Formats and prints the SEM model syntax generated by GenerateModelCN, GenerateModelCP, GenerateModelP, and GenerateModelPC. It organizes the equations into labeled sections for better readability.

Usage

printGM(x, ...)

Arguments

Value

Invisibly returns the formatted SEM model syntax.

Examples

data(example_data)
head(example_data)
prepared_data <- PrepareData(
  data = example_data,
  M_C1 = c("A1", "B1"),
  M_C2 = c("A2", "B2"),
  Y_C1 = "C1",
  Y_C2 = "C2"
)
sem_model <- GenerateModelPC(prepared_data)
printGM(sem_model)

Resolve dependencies of defined parameters

Description

Recursively resolves all free parameters involved in each defined parameter.

Usage

resolve_all_dependencies(def_map)

Arguments

Value

A list mapping each defined parameter to its dependent free parameters.

Run Monte Carlo-based mediation inference

Description

Performs Monte Carlo simulation to estimate confidence intervals for both unstandardized and (optionally) standardized mediation parameters, based on fitted SEM models.

Usage

run_mc_mediation(
  fit,
  data,
  standardized = FALSE,
  R = 20000,
  seed = 123,
  alpha = 0.05,
  alphastd = 0.05
)

Arguments

Value

A list with unstandardized and (if requested) standardized results. Each result includes Monte Carlo estimates, SEs, and CIs.

Sort parameters for printing in SEM output

Description

Sorts a parameter table by conceptual priority for presentation purposes. This function is designed to support formatted output of mediation and SEM results by organizing parameters such as a-paths, b-paths, indirect effects, contrasts, etc.

Usage

sort_parameters(df)

Arguments

Details

This is an internal helper function used by print.WsMed() to ensure that printed tables of standardized or unstandardized estimates appear in a logical and human-readable order.

Value

A reordered version of the same data frame, with rows sorted according to a predefined logical structure:

1. a-paths (e.g., a1, a2, ...)

2. b-paths (e.g., b1, b2, ...)

3. d-paths (e.g., d1, d2, ...)

4. indirect effects (e.g., ind1, ind2, ...)

5. direct effect

6. total indirect

7. total effect

8. contrasts (e.g., ind1-ind2, ind2-ind3)

9. X-condition path terms (e.g., X1_b1, X0_b1, ...)

Standardize conditional mediation results using existing joint draws

Description

Standardize conditional mediation results using existing joint draws

Usage

standardize_moderation(object)

Arguments

Details

Conditional indirect and total effects are divided by the marginal model-implied SD of Ydiff. Conditional paths use their endpoint scales. Moderator probe values remain fixed in raw units; continuous interaction coefficients in mod_coeff are reported per SD of W. Dummy variables retain 0/1 units. Coefficients and scales are transformed jointly for each draw. Intervals are percentile intervals, as in the raw conditional tables. Metadata attributes record scales, probe conventions and draw diagnostics. With fixed.x=TRUE, external moments are held fixed. MI uses pooled primitive parameters and the existing first-imputation probing reference. For models with endogenous products, marginal variances are pooled jointly with coefficients before taking square roots, retaining their sampling covariance. Saved endogenous-product fits and MI draws require compatible algorithm identifiers. Refit unversioned old models rather than restandardizing them.

Value

A moderation list with the same conditional tables and curves as the raw output, or a list with mc and boot components for ci_method="both".

Summarize Monte Carlo simulation results

Description

Computes summary statistics for Monte Carlo simulation results, including the mean estimate, standard error (SE), and confidence intervals (CIs).

Usage

summarize_mc_ci(mc_result, alpha = 0.05)

Arguments

Value

A data frame with one row per parameter and the following columns:

Parameter

The name of the parameter.

Estimate

The average estimate across all Monte Carlo replications.

SE

The standard deviation of the estimates (standard error).

CI_lower

The lower bound of the confidence interval.

CI_upper

The upper bound of the confidence interval.

Validate user inputs for wsMed()

Description

All checks stop() with an informative message when they fail. Invisibly returns TRUE on success.

Usage

validate_wsMed_inputs(
  data,
  M_C1,
  M_C2,
  Y_C1,
  Y_C2,
  C_C1 = NULL,
  C_C2 = NULL,
  C = NULL,
  W = NULL,
  W_type = NULL,
  MP = NULL,
  form = c("P", "CN", "CP", "PC", "UD"),
  paths = NULL,
  Na = c("DE", "FIML", "MI"),
  R = 20000L,
  bootstrap = 1000L,
  m = 5L,
  ci_level = 0.95,
  ci_method = NULL,
  MCmethod = NULL
)

Analyze two-condition within-subject mediation

Description

wsMed() fits a structural equation model (SEM) for two-condition within-subject mediation. It can handle missing data (DE, FIML, MI) and computes both unstandardized and standardized effects with bootstrap or Monte Carlo confidence intervals.

Usage

wsMed(
  data,
  M_C1,
  M_C2,
  Y_C1,
  Y_C2,
  C_C1 = NULL,
  C_C2 = NULL,
  C = NULL,
  C_type = NULL,
  W = NULL,
  W_type = NULL,
  MP = NULL,
  form = c("P", "CN", "CP", "PC", "UD"),
  Na = c("DE", "FIML", "MI"),
  alpha = 0.05,
  mi_args = list(),
  R = 20000L,
  bootstrap = 2000,
  boot_ci_type = "perc",
  iseed = 123,
  fixed.x = FALSE,
  ci_method = c("mc", "bootstrap", "both"),
  MCmethod = NULL,
  seed = 123,
  standardized = FALSE,
  verbose = FALSE,
  paths = NULL
)

Arguments

Details

Model structures:

• "P": parallel mediation

• "CN": chained (serial) mediation

• "CP": chained then parallel

• "PC": parallel then chained

• "UD": user-defined mediation model

Missing-data strategies:

• "DE": list-wise deletion

• "FIML": full-information maximum likelihood

• "MI": multiple imputation via mice

Confidence-interval engines:

• Bootstrap: participant resampling (DE and FIML only)

• Monte Carlo: joint parameter draws via semmcci for DE/FIML, or the package's pooled-parameter sampler for MI

Monte Carlo inference uses the existing joint-parameter sampler. The legacy MCmethod = "bootSD" option was accepted but never applied; it now produces an explicit error rather than silently returning ordinary MC results.

Standardization divides differences by their marginal model-implied SDs, without recentering them. Dummy variables retain 0/1 units; interaction terms use the product of their component scale factors, not the interaction column SD. MC and bootstrap draws are transformed jointly with their scales. Current effect reports from print, summary and wsmed_effects, and confint applied to extracted effects, use percentile intervals (type-7 quantiles), with or without moderation. The boot_ci_type argument controls stored legacy parameter-table intervals, not these effect reports. In standardized legacy tables, bc and bca.simple apply bias correction with zero acceleration, not full BCa. Their bootstrap p-values require at least 1000 valid replicates.

Conditional tables use percentile intervals and fixed numerical probes. With fixed.x=TRUE, scale transformations condition on the fitted external moments rather than adding sampling uncertainty for those moments. In MI, probes/centering references use the first completed data set; fixed.x=TRUE also conditions on its external moments in the pooled transform. In MI models with products of mediator differences and the moderator, marginal variances are pooled jointly with primitive coefficients, including their delta-method within-imputation and Rubin between-imputation covariance. MC propagates this joint uncertainty. Endogenous product moments are estimated even with fixed.x=TRUE.

Workflow: (1) preprocess -> (2) generate SEM syntax -> (3) fit -> (4) compute confidence intervals -> (5) optional: standardize estimates. See wsmed_categorical for supported categorical predictors, reference coding, and missing-data restrictions. In both interfaces, declare categorical covariates and moderators as unordered factors with explicit levels before fitting. The first factor level is the reference group. Existing automatic recognition of numeric 0/1 predictors is retained in this one-call interface. The empirical manuscript dataset is available as wsmed_examples; example_data is simulated tutorial data.

The one-call interface uses the same fitting and inference engines as wsmed_model(), wsmed_fit(), and wsmed_infer(). Default printing is concise; print(result, detail = "full") adds path coefficients to the structured effect report. Use detail = "legacy" for the original tables. Use wsmed_effects() to query stored draws without rerunning the analysis.

Value

An object of class "wsMed" with elements:

data

Preprocessed data frame.

sem_model

Generated lavaan syntax.

mc

List with Monte Carlo draws, bootstrap tables (if any), and the fitted model.

moderation

Raw conditional or moderated effect tables.

moderation_std

Standardized conditional tables and curves when standardized=TRUE and a moderator is supplied.

form,Na,alpha

Analysis settings.

paths

The user-defined paths when form = "UD"; otherwise NULL.

input_vars

Names of all user-supplied variables.

model,fit,inference

Structured specification, reusable fit (including all imputation fits), and named stored inference objects.

Examples

data("example_data", package = "wsMed")
result <- wsMed(
  data = example_data,
  M_C1 = c("A1", "B1"),
  M_C2 = c("A2", "B2"),
  Y_C1 = "D1",
  Y_C2 = "D2"
)
print(result)

Categorical predictors and supported variable types

Description

wsMed supports binary and multicategory between-subject covariates and one categorical moderator in models with numeric outcomes and mediators.

Supported roles

Outcomes, mediators, and within-subject covariates must be numeric measures analyzed by the continuous-response SEM. Binary or ordinal response models, thresholds, and categorical links are not implemented. Storing a binary outcome as numbers does not turn the estimator into a categorical-response model. Between-subject covariates and the single moderator may instead be unordered factors. Ordered factors are rejected by wsmed_fit(); explicitly convert them to unordered factors only if a nominal interpretation is intended.

Declaring categories

In both the one-call and staged workflows, use base::factor() to declare categorical between-subject covariates and moderators in the input data. Specify levels explicitly: the first level is the reference group. For a binary predictor, factor(x, levels = c(0, 1)) selects 0 as the reference; reversing the levels selects 1. Use unordered factors for nominal categories. Every declared factor level must have observations in the supplied data.

The existing default recognition rules are retained for compatibility. wsmed_fit() treats numeric predictors, including 0/1 columns, as continuous. The one-call wsMed() and PrepareData() interfaces retain automatic detection: factors, character columns, and numeric columns whose observed values are exactly 0 and 1 are categorical. Other numeric columns are continuous unless C_type or W_type overrides detection. Explicit factors provide the same categorical interpretation and reference coding in both interfaces without relying on automatic recognition. The one-call type arguments remain available to override its default rules.

Reference coding and comparisons

A factor with K levels is represented by K - 1 treatment indicators. The first factor level is the reference; stats::relevel() changes it before fitting. Indicators are not mean-centered. Coding does not depend on the global contrasts option. Between-subject indicators use Cb* names and moderator indicators use W* names. The C_info and W_info attributes of prepared data record their mappings. Labels such as low vs high identify the reference and comparison level: the indicator is 0 at low and 1 at high, so its coefficient is high minus low.

wsmed_effects() accepts original factor labels in at; by default it extracts every level. wsmed_contrasts() compares effects using their joint draws, preserving covariance. Do not subtract the endpoints of separate confidence intervals to obtain an interval for a difference. Marginally standardized conditional effects use the common model-implied outcome-difference SD, not separate within-category SDs; factor labels and dummy indicators retain their coding. See standardize_moderation(). Generated models account for the dependence of endogenous products on upstream mediator disturbances. Reference changes preserve the implied marginal scales of the same fitted model. MI pools marginal variance estimates jointly with coefficients; compare the same completed datasets when checking recoding. Refit saved models from before this covariance correction.

Missing values

The staged default missing = "error" rejects missing analysis values. Listwise deletion and multiple imputation are available for incomplete categorical predictors. FIML is allowed with fully observed categorical predictors but wsmed_fit() rejects missing categorical predictors; the one-call interface should follow the same restriction. MI imputes the raw variables before constructing dummy variables and interactions. The staged mi$method argument selects the method for numeric variables (default "pmm"). Factors use logistic regression for two levels and multinomial logistic regression for more than two levels. This differs from the lower-level ImputeData() method argument. MI supports Monte Carlo inference only.

See Also

wsmed_model(), wsmed_fit(), wsmed_effects(), wsmed_contrasts(), PrepareData(), ImputeData()

Examples

data(example_data)
dat <- example_data
dat$Group <- factor(dat$Group, levels = c("low", "med", "high"))
prepared <- PrepareData(dat, M_C1 = "A1", M_C2 = "A2",
                        Y_C1 = "D1", Y_C2 = "D2",
                        W = "Group", W_type = "categorical")
attr(prepared, "W_info")

Contrast effects using their joint stored draws

Description

Contrast effects using their joint stored draws

Usage

wsmed_contrasts(object, contrasts, level = object$query$level)

Arguments

Value

A wsmed_results object. Intervals are percentile intervals of the jointly evaluated contrasts, not differences between confidence limits.

Extract effects from fitted models or stored inference

Description

Extract effects from fitted models or stored inference

Usage

wsmed_effects(
  object,
  type = c("indirect", "all", "total", "direct", "total_indirect", "paths", "parameters"),
  terms = NULL,
  at = NULL,
  scale = NULL,
  method = NULL,
  level = NULL,
  standardized = NULL
)

Arguments

Details

Extraction never fits, imputes, or samples. Effect and contrast intervals are percentile intervals from the stored joint draws, including when bootstrap parameter tables were requested with bias correction. For conditional standardization, moderator probes remain fixed in raw units. Scales and coefficients vary jointly across draws. Invalid standardized draws are excluded jointly and their original identities are recorded. For MI the existing first-imputation reference and per-imputation centering are retained. Endogenous products have an auxiliary moment model that retains the necessary upstream residual covariances. MI moderated models jointly pool marginal variances with coefficients, using a delta-method within-imputation covariance and Rubin's rules; MC draws retain their covariance. Refit objects saved before this correction. Finite MC draws or new imputations need not match after recoding. The query interpretation records the plug-in outcome-difference SD. Conditional result tables include extrapolated flags relative to observed moderator values among used participants (imputed values excluded). Out-of-range continuous probes signal wsmed_extrapolation_warning once per extraction; this checks the marginal range, not joint covariate overlap or data density. Categorical tables include n.observed, n.fitted.min, and n.fitted.max; for MI the latter span the completed datasets and are not effective sample sizes. The descriptive support metadata are retained in query$support. Incompatible saved fits stop before extraction. If a fit is compatible but its MI sampler is unversioned or outdated, rerun wsmed_infer() on that fit. Stored primitive coefficients and full legacy tables remain inspectable.

Value

A wsmed_results object with a full-precision table, joint effect draws, draw identities, query settings, and diagnostics.

Data for the manuscript examples

Description

The prepared analysis data used in the three wsMed manuscript examples, derived from the iWeek study deposited by Sagaribay (2025). Each row is one participant; T1 and T2 denote measurements before and after the study period. The examples use T2 minus T1 as the within-person contrast.

Usage

wsmed_examples

Format

A data frame with 123 rows and 14 numeric columns:

TSRQ_T1, TSRQ_T2

Autonomous motivation scores at T1 and T2.

WEMBS_T1, WEMBS_T2

Subjective well-being scores at T1 and T2.

SMUF_T1, SMUF_T2

Daily social media use scores at T1 and T2.

ISI_T1, ISI_T2

Insomnia severity scores at T1 and T2.

DASSS_T1, DASSS_T2

DASS stress-subscale scores at T1 and T2, each calculated as the sum of the seven stress items.

HES_T1, HES_T2

Healthy eating scores at T1 and T2.

Age

Age in years.

BMI

Body mass index, recalculated from weight in pounds and height in inches using 703 * weight / height^2.

Details

Score columns retain their prepared source values without rescaling or standardization. The original dataset contains 221 participants: 123 in the iWeek intervention group and 98 in the control group. This prepared dataset retains the 123 intervention-group participants. The wsMed examples illustrate within-person mediation using their paired T1/T2 measurements; they do not reproduce the original study's group comparison.

The manuscript authors retained the 14 analysis columns and recalculated BMI. Height values outside the preparation rule of 48–84 inches, including ambiguous units, were not converted by guesswork. BMI remains missing for 13 such records. No further participants were removed, no values were imputed, and no additional cleaning is performed when creating this package dataset. SPSS storage attributes are omitted; numerical values, row order, and missing positions match the prepared wsMed_examples.sav replication file.

Across these 14 columns, 55 participants (44.7 percent) have at least one missing value, with 146 missing cells in total. Missing counts for paired T1/T2 columns are TSRQ: 1/22; WEMBS: 0/22; SMUF: 8/18; ISI: 0/19; DASSS: 0/23; HES: 0/20. Age is complete and BMI has 13 missing values. Counts for an individual analysis depend on the variables it selects.

In Examples 1 and 3, TSRQ, WEMBS, and SMUF are mediators; in Example 2, WEMBS and ISI precede TSRQ. DASSS is the outcome, HES is a within-subject covariate, and BMI is a between-subject covariate. Age is a covariate in Examples 1 and 2 and the moderator in Example 3.

The source data and this prepared derivative are distributed under CC BY 4.0, separately from the package code's GPL license. Attribution and modification information are also installed in ‘COPYRIGHTS’.

Source

Sagaribay, R. (2025). iWeek: A Week-Long Social Media Abstinence Intervention and its Impact on Well-Being, Mental Health, Body Image, and Sleep in Latina College Students. Mendeley Data, version 1. doi:10.17632/fn9n95bsh5.1. License: https://creativecommons.org/licenses/by/4.0/.

See Also

example_data(), wsMed(), wsmed_fit()

Examples

data(wsmed_examples)
dim(wsmed_examples)
colSums(is.na(wsmed_examples))
sum(!complete.cases(wsmed_examples))

Fit a specified mediation model without simulation

Description

Fit a specified mediation model without simulation

Usage

wsmed_fit(
  model,
  data,
  missing = c("error", "listwise", "fiml", "mi"),
  mi = list(),
  fixed.x = FALSE,
  verbose = FALSE,
  standardized = FALSE
)

Arguments

Details

This release preserves the existing per-imputation centering and first-imputation reporting reference. It does not implement MI-bootstrap. Factor outcome, mediator, and within-subject covariate columns are unsupported. See wsmed_categorical for reference coding, numeric 0/1 predictors, and restrictions on missing categorical predictors. mi$method specifies the method for numeric variables; factors use logistic or multinomial regression. Default imputation uses main effects and need not preserve substantive-model interactions. For moderated models, consider model-compatible external imputation (e.g., SMC-FCS) and pass its completed datasets. Supplied datasets must preserve participant order, row names, observed values and factor levels, and complete all analysis variables. Difference scores and products are rebuilt separately in every dataset, then the same Rubin/MC engine is used. Validation does not establish imputation-model compatibility. Save the external imputer's settings, convergence diagnostics and software versions separately. Internal MI with moderator interactions signals one wsmed_mi_compatibility_warning per fit and records it in fit diagnostics. Numeric 0/1 moderators remain continuous in this interface; a message explains this choice. For categorical predictors in either interface, use unordered factors with explicit levels; the first level is the reference group. Type messages are retained as diagnostics$input_notes and do not change estimates or random-number state. Diagnostics include dataset-level convergence/admissibility, covariance checks, case counts, warnings, and fitted category counts. Complete-case matrix rank is descriptive, not an identification test for FIML. Counts below five receive a descriptive small-category flag, not a universal validity cutoff. Constant transformed variables are named in pre-fit errors. Non-converged MI datasets stop pooling; known inadmissible fits cannot enter inference.

Value

A wsmed_fit containing all fits and pooled estimates for MI.

Apply uncertainty quantification to an existing fit

Description

Apply uncertainty quantification to an existing fit

Usage

wsmed_infer(
  object,
  method = c("mc", "bootstrap"),
  draws = NULL,
  seed = NULL,
  level = 0.95,
  interval = "perc",
  decomposition = "eigen",
  pd = TRUE,
  tol = 1e-06
)

Arguments

Details

Inference never reimputes or changes the supplied fit. Bootstrap necessarily fits resampled datasets. No new statistical engine is introduced. Seeded calls restore the caller's random state; seed = NULL consumes the current random stream and advances it normally. Fits with endogenous products must record compatible model and MI pooling algorithm identifiers. Older unversioned moderated fits require refitting from the original data; package version alone cannot identify their algorithms.

Value

A wsmed_inference storing draws and the original fit.

Inspect a modular workflow without recomputation

Description

Inspect a modular workflow without recomputation

Usage

wsmed_inspect(
  object,
  component = c("model", "syntax", "data", "fits", "diagnostics", "provenance")
)

Arguments

Value

The requested specification, syntax, data, backend fits, or metadata.

Methods for modular wsMed objects

Description

Methods for modular wsMed objects

Usage

## S3 method for class 'wsMed'
print(
  x,
  digits = 3,
  detail = c("effects", "full", "overview", "legacy"),
  type = "all",
  at = NULL,
  standardized = NULL,
  method = NULL,
  level = NULL,
  scale = NULL,
  ...
)

## S3 method for class 'wsmed_model'
print(x, ...)

## S3 method for class 'wsmed_fit'
print(x, digits = 3, ...)

## S3 method for class 'wsmed_inference'
print(x, ...)

## S3 method for class 'wsmed_fit'
summary(object, ...)

## S3 method for class 'summary_wsmed_fit'
print(x, digits = 3, ...)

## S3 method for class 'wsmed_inference'
summary(
  object,
  type = "all",
  at = NULL,
  scale = NULL,
  standardized = NULL,
  ...
)

## S3 method for class 'wsMed'
summary(
  object,
  type = "all",
  at = NULL,
  scale = NULL,
  method = NULL,
  standardized = NULL,
  ...
)

## S3 method for class 'wsmed_fit'
coef(object, ...)

## S3 method for class 'wsmed_fit'
vcov(object, ...)

## S3 method for class 'wsmed_fit'
nobs(object, ...)

## S3 method for class 'wsmed_inference'
coef(object, ...)

## S3 method for class 'wsmed_inference'
vcov(object, ...)

## S3 method for class 'wsmed_inference'
nobs(object, ...)

## S3 method for class 'wsMed'
coef(object, ...)

## S3 method for class 'wsMed'
vcov(object, ...)

## S3 method for class 'wsMed'
nobs(object, ...)

## S3 method for class 'wsmed_fit'
confint(object, parm, level = NULL, ...)

## S3 method for class 'wsmed_inference'
confint(object, parm, level = NULL, standardized = NULL, scale = NULL, ...)

## S3 method for class 'wsMed'
confint(
  object,
  parm,
  level = NULL,
  method = NULL,
  standardized = NULL,
  scale = NULL,
  ...
)

## S3 method for class 'wsmed_results'
print(x, digits = 3, ...)

## S3 method for class 'wsmed_results'
summary(object, level = NULL, ...)

## S3 method for class 'wsmed_results'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

## S3 method for class 'wsmed_results'
coef(object, ...)

## S3 method for class 'wsmed_results'
vcov(object, ...)

## S3 method for class 'wsmed_results'
confint(object, parm, level = NULL, ...)

## S3 method for class 'summary_wsmed'
print(x, digits = 3, ...)

## S3 method for class 'summary_wsmed'
summary(object, level = NULL, ...)

Arguments

Value

print methods return their input invisibly. summary returns a summary_wsmed (also a wsmed_results) for inference/one-call objects, or a summary_wsmed_fit for fits. Structured summaries contain effects and coefficients data frames, info, diagnostics and selected joint effect draws. as.data.frame, coef, confint, vcov and plot on the summary concern the selected effects; coefficients contains conditional path slopes (not all primitive parameters). Use type = 'parameters' for primitive parameters. Summary display uses printCoefmat; tables retain full precision. coef returns a named numeric vector. vcov returns the fit covariance (Rubin-pooled for MI), or the empirical covariance of stored draws for inference/results. confint recomputes percentile limits from stored draws without sampling. Its default level is the saved level, including when called on an inference or one-call object; use an explicit level to override it. On those two classes confint concerns native primitive parameters, matching coef and vcov regardless of the reporting preference (explicit scale overrides are accepted). Fit-only summaries also describe native parameters. Extract effects first to obtain indirect/conditional-effect intervals. nobs returns the sample size for each fitted dataset. as.data.frame returns a full-precision results table. plot returns a ggplot.

See Also

wsmed_reproducibility() for an exportable analysis manifest.

Specify a two-condition mediation model

Description

Specify a two-condition mediation model

Usage

wsmed_model(
  outcome,
  mediators,
  conditions = names(outcome),
  covariates = list(),
  structure = "parallel",
  paths = NULL,
  moderator = NULL
)

Arguments

Details

All moderator main effects are explicitly included in all difference equations. Difference-slope and average-slope interactions are separate. Categorical between-subject predictors must be unordered factors in the staged workflow. Use the same explicit factor declaration with wsMed(). Set factor levels explicitly; the first level is the reference group.

Value

A wsmed_model specification, without data or fitted estimates.

See Also

wsmed_categorical for supported variable roles and factor coding.

Plot mediation effects from workflow objects

Description

Plot stored effects without fitting, imputing, or drawing new samples. The returned ggplot contains the full-precision plotted table in data.

Usage

## S3 method for class 'wsMed'
plot(
  x,
  type = "indirect",
  terms = NULL,
  at = NULL,
  scale = NULL,
  method = NULL,
  level = NULL,
  view = c("auto", "forest", "curve", "conditional"),
  n = 51L,
  labels = NULL,
  title = NULL,
  x_label = NULL,
  y_label = NULL,
  ncol = NULL,
  base_size = 12,
  rug = FALSE,
  standardized = NULL,
  ...
)

## S3 method for class 'wsmed_inference'
plot(
  x,
  type = "indirect",
  terms = NULL,
  at = NULL,
  scale = NULL,
  method = NULL,
  level = NULL,
  view = c("auto", "forest", "curve", "conditional"),
  n = 51L,
  labels = NULL,
  title = NULL,
  x_label = NULL,
  y_label = NULL,
  ncol = NULL,
  base_size = 12,
  rug = FALSE,
  standardized = NULL,
  ...
)

## S3 method for class 'wsmed_fit'
plot(
  x,
  type = "indirect",
  terms = NULL,
  at = NULL,
  scale = NULL,
  method = NULL,
  level = NULL,
  view = c("auto", "forest", "curve", "conditional"),
  n = 51L,
  labels = NULL,
  title = NULL,
  x_label = NULL,
  y_label = NULL,
  ncol = NULL,
  base_size = 12,
  rug = FALSE,
  standardized = NULL,
  ...
)

## S3 method for class 'wsmed_results'
plot(
  x,
  view = c("auto", "forest", "curve", "conditional"),
  terms = NULL,
  level = NULL,
  labels = NULL,
  title = NULL,
  x_label = NULL,
  y_label = NULL,
  ncol = NULL,
  base_size = 12,
  ...
)

Arguments

Details

Curves connect finite probes; bands are pointwise percentile intervals, not simultaneous bands or Johnson–Neyman regions. Supplied probes outside the observed fitted-case range are identified in the caption and plotted data (extrapolated). Group counts appear in categorical captions; for MI these are ranges across completed datasets, not effective sample sizes. Plot metadata retain support, including the observed range or group counts. remain in raw units. A continuous conditional point plot requires explicit at values. Forest plots without at use the usual reference value. Category and effect selections retain their supplied order. A fitted object without inference produces point estimates only, with no confidence band.

Extracted results contain only their selected effects and probes: plotting them never invents a new grid. Use wsmed_effects() again to change the scale, method, or probes. wsmed_contrasts() constructs differences from joint draws. Marginal standardization uses the same draw-specific scales as effect extraction; it does not standardize a plotted table after computing intervals.

The older plot_effects(), plot_conditional_effects(), plot_contrasts(), and plot_moderation_curve() calls remain available. For wsMed objects those helpers preserve the stored legacy tables, including requested bootstrap interval types. The plot methods here use the percentile convention of wsmed_effects(). Plots explicitly identify their interval source.

Value

A ggplot. Its wsmed_plot attribute records the method, scale, confidence level, interval convention, view, and automatic-grid range.

See Also

wsmed_effects(), wsmed_contrasts(), wsmed_methods

Examples

data(example_data)
model <- wsmed_model(c(before = "D1", after = "D2"),
  list(A = c(before = "A1", after = "A2")),
  moderator = list(variable = "D3", interactions = "A -> Y"))
fit <- wsmed_fit(model, example_data)
plot(fit, n = 21, title = "Conditional point estimates")

Export the settings and provenance of an analysis

Description

Export the settings and provenance of an analysis

Usage

wsmed_reproducibility(object, file = NULL)

Arguments

Details

This report contains no participant-level data or simulation draws. It is an analysis manifest, not a replacement for the data, analysis script, or a dependency lockfile. It never fits, imputes, samples, or updates results. Recorded fitting versions are separated from the environment used to export. Metadata absent from an older saved object remain absent, not reconstructed from currently installed package versions. Reports for extracted results include the query and contrast definition as well as the upstream settings.

Value

A plain list of model specification, settings, reference coding, diagnostics, and recorded software versions; invisibly when written to file.

Examples

data(example_data)
m <- wsmed_model(c(before = "D1", after = "D2"),
  list(A = c(before = "A1", after = "A2")))
f <- wsmed_fit(m, example_data)
report <- wsmed_reproducibility(f)
report$analysis$missing