Within-Subject Mediation Analysis (Two-Condition)

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"),
  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
)

Arguments

data

A data.frame containing the raw scores.

M_C1, M_C2

Character vectors of mediator names under condition 1 and 2.

Y_C1, Y_C2

Character scalars for the outcome under each condition.

C_C1, C_C2

Character vectors of within-subject covariates (per condition).

C

Character vector of between-subject covariates.

C_type

Character; type of C: "continuous" or "categorical".

W

Character vector of moderators. Default NULL.

W_type

Character; "continuous" or "categorical".

MP

Character vector identifying which regression paths are moderated (for example, "a1", "b_1_2", "cp").

form

Model type: "P", "CN", "CP", or "PC".

Na

Missing-data method: "DE", "FIML", or "MI".

alpha

Numeric vector in (0, 1); two-sided significance levels.

mi_args

List of MI-specific controls:

m: Number of imputations. Default 5.
method_num: Imputation method for mice().
decomposition: Covariance-decomposition method ("eigen", "chol", "svd").
pd: Logical; positive-definiteness check.
tol: Tolerance for the positive-definiteness check.

R

Integer; number of Monte Carlo draws. Default 20000L.

bootstrap

Integer; number of bootstrap replicates (DE and FIML only).

boot_ci_type

Character; bootstrap CI type: "perc", "bc", or "bca.simple".

iseed, seed

Integer seeds for bootstrap and Monte Carlo, respectively.

fixed.x

Logical; passed to lavaan.

ci_method

CI engine: "bootstrap" or "mc". If NULL (default) the choice is "bootstrap" for Na = "DE" and "mc" otherwise.

MCmethod

If Na = "FIML" and ci_method = "mc", choose "mc" (default) or "bootSD".

standardized

Logical; if TRUE, return standardized effects. Default FALSE.

verbose

Logical; print progress messages.

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: Conditional or moderated effect tables.
form,Na,alpha: Analysis settings.
input_vars: Names of all user-supplied variables.

Details

Model structures:

"P": parallel mediation
"CN": chained (serial) mediation
"CP": chained then parallel
"PC": parallel then chained

Missing-data strategies:

"DE": list-wise deletion
"FIML": full-information maximum likelihood
"MI": multiple imputation via mice

Confidence-interval engines:

Bootstrap: percentile, BC, or BCa (DE and FIML only)
Monte Carlo: draws via semmcci (all Na options)

For Na = "FIML", you may choose MCmethod = "mc" (default) or "bootSD" to add a finite-sample SD correction.

Workflow: (1) preprocess -> (2) generate SEM syntax -> (3) fit -> (4) compute confidence intervals -> (5) optional: standardize estimates.

Examples

data("example_data", package = "wsMed")
set.seed(123)
result <- wsMed(
  data = example_data,
  M_C1 = c("A2", "B2"),
  M_C2 = c("A1", "B1"),
  Y_C1 = "C1", Y_C2 = "C2",
  form = "P", Na = "DE"
)
print(result)
#> 
#> 
#> *************** VARIABLES ***************
#> Outcome (Y):
#>    Condition 1: C1 
#>    Condition 2: C2 
#> Mediators (M):
#>   M1:
#>     Condition 1: A2
#>     Condition 2: A1
#>   M2:
#>     Condition 1: B2
#>     Condition 2: B1
#> Sample size (rows kept): 100 
#> 
#> 
#> *************** MODEL FIT ***************
#> 
#> 
#> |Measure   |  Value|
#> |:---------|------:|
#> |Chi-Sq    | 11.436|
#> |df        |  5.000|
#> |p         |  0.043|
#> |CFI       |  0.000|
#> |TLI       | -1.130|
#> |RMSEA     |  0.113|
#> |RMSEA Low |  0.018|
#> |RMSEA Up  |  0.202|
#> |SRMR      |  0.076|
#> 
#> 
#> ************* TOTAL / DIRECT / TOTAL-IND (MC) *************
#> 
#> 
#> |Label          | Estimate|    SE| 2.5%CI.Lo| 97.5%CI.Up|
#> |:--------------|--------:|-----:|---------:|----------:|
#> |Total effect   |    0.015| 0.016|    -0.017|      0.047|
#> |Direct effect  |    0.016| 0.016|    -0.016|      0.048|
#> |Total indirect |   -0.001| 0.004|    -0.009|      0.008|
#> 
#> Indirect effects:
#> 
#> 
#> |Label | Estimate|    SE| 2.5%CI.Lo| 97.5%CI.Up|
#> |:-----|--------:|-----:|---------:|----------:|
#> |ind_1 |    0.001| 0.003|    -0.005|      0.008|
#> |ind_2 |   -0.002| 0.003|    -0.009|      0.003|
#> 
#> Indirect-effect key:
#> 
#> 
#> |Ind   |Path                 |
#> |:-----|:--------------------|
#> |ind_1 |X -> M1diff -> Ydiff |
#> |ind_2 |X -> M2diff -> Ydiff |
#> 
#> 
#> *************** MODERATION EFFECTS (d-paths, MC) ***************
#> 
#> 
#> |Coefficient | Estimate|    SE| 2.5%CI.Lo| 97.5%CI.Up|
#> |:-----------|--------:|-----:|---------:|----------:|
#> |d1          |   -0.062| 0.091|    -0.242|      0.116|
#> |d2          |   -0.073| 0.083|    -0.235|      0.090|
#> 
#> 
#> *************** MODERATION KEY (d-paths) ***************
#> 
#> 
#> |Coefficient |Path           |Moderated       |
#> |:-----------|:--------------|:---------------|
#> |d1          |M1avg -> Ydiff |M1diff -> Ydiff |
#> |d2          |M2avg -> Ydiff |M2diff -> Ydiff |
#> 
#> 
#> *************** CONTRAST INDIRECT EFFECTS (No Moderator) ***************
#> 
#> 
#> |Contrast                  | Estimate|    SE| 2.5%CI.Lo| 97.5%CI.Up|
#> |:-------------------------|--------:|-----:|---------:|----------:|
#> |indirect_2  -  indirect_1 |   -0.003| 0.004|    -0.012|      0.005|
#> 
#> 
#> *************** C1-C2 COEFFICIENTS (No Moderator) ***************
#> 
#> 
#> |Coeff | Estimate|    SE| 2.5%CI.Lo| 97.5%CI.Up|
#> |:-----|--------:|-----:|---------:|----------:|
#> |X1_b1 |    0.005| 0.101|    -0.191|      0.202|
#> |X0_b1 |    0.068| 0.102|    -0.134|      0.266|
#> |X1_b2 |    0.077| 0.098|    -0.116|      0.270|
#> |X0_b2 |    0.150| 0.099|    -0.045|      0.344|
#> 
#> 
#> *************** REGRESSION PATHS (MC) ***************
#> 
#> 
#> |Path           |Label | Estimate|    SE| 2.5%CI.Lo| 97.5%CI.Up|
#> |:--------------|:-----|--------:|-----:|---------:|----------:|
#> |Ydiff ~ M1diff |b1    |    0.036| 0.091|    -0.141|      0.215|
#> |Ydiff ~ M1avg  |d1    |   -0.062| 0.091|    -0.242|      0.116|
#> |Ydiff ~ M2diff |b2    |    0.112| 0.090|    -0.061|      0.291|
#> |Ydiff ~ M2avg  |d2    |   -0.073| 0.083|    -0.235|      0.090|
#> 
#> 
#> *************** INTERCEPTS (MC) ***************
#> 
#> 
#> |Intercept |Label | Estimate|    SE| 2.5%CI.Lo| 97.5%CI.Up|
#> |:---------|:-----|--------:|-----:|---------:|----------:|
#> |Ydiff~1   |cp    |    0.016| 0.016|    -0.016|      0.048|
#> |M1diff~1  |a1    |    0.027| 0.018|    -0.007|      0.062|
#> |M2diff~1  |a2    |   -0.014| 0.018|    -0.049|      0.020|
#> |M1avg~1   |      |   -0.000| 0.018|    -0.035|      0.036|
#> |M2avg~1   |      |    0.000| 0.020|    -0.040|      0.040|
#> 
#> 
#> *************** VARIANCES (MC) ***************
#> 
#> 
#> |Variance       |Label | Estimate|    SE| 2.5%CI.Lo| 97.5%CI.Up|
#> |:--------------|:-----|--------:|-----:|---------:|----------:|
#> |Ydiff~~Ydiff   |      |    0.026| 0.004|     0.019|      0.033|
#> |M1diff~~M1diff |      |    0.031| 0.004|     0.022|      0.039|
#> |M2diff~~M2diff |      |    0.032| 0.005|     0.023|      0.041|
#> |M1avg~~M1avg   |      |    0.034| 0.005|     0.025|      0.043|
#> |M2avg~~M2avg   |      |    0.041| 0.006|     0.029|      0.052|