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Prepare Data for Two-Condition Within-Subject Mediation (WsMed)

Source: R/PrepareData.R
PrepareData.Rd

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

data

A data frame with the raw pre/post measures.

M_C1, M_C2

Character vectors: mediator names at occasion 1 and 2 (equal length).

Y_C1, Y_C2

Character scalars: outcome names at occasion 1 and 2.

C_C1, C_C2

Optional character vectors: within-subject control names.

C

Optional character vector: between-subject control names.

C_type

Optional vector of the same length as C. Each element is one of "continuous", "categorical", or "auto" (default). Ignored when C = NULL.

W

Optional character vector: moderator names (one or more).

W_type

Optional vector of the same length as W. Same coding as C_type. Ignored when W = NULL.

center_W

Logical. Whether to center the moderator variable W.

keep_W_raw, keep_C_raw

Logical. If TRUE, keep the original W / C columns in the returned data.

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.

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. Moderators W (one or more):

    • 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).

See also

PrepareMissingData, GenerateModelP, wsMed

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)
#>   W_bin W_fac3       Ydiff     M1diff      M2diff      M1avg       M2avg Cb1_1
#> 1     0    Med -0.27317995  1.0245597  1.07055378 -0.2230613 -0.04728171     0
#> 2     0    Low -1.28170568 -0.7956697 -0.06067571 -0.3230789  0.04958610     0
#> 3     1    Low -1.65624325  1.1767483  0.59285327 -0.3561418 -0.57668693     1
#> 4     1    Med  0.08324346 -2.7246439 -1.08739092  0.1240715 -0.34785861     1
#> 5     0    Low  2.98078634  1.1035159 -0.83287567  0.7723784 -1.03321440     0
#> 6     0    Med -1.35143323  2.8008683 -2.84247957  0.4710784  0.38385556     0
#>       Cw1diff     Cw1avg W1 W2 int_M1diff_W1 int_M1diff_W2 int_M2diff_W1
#> 1  0.69594079  1.1321756  0  1     0.0000000      1.024560    0.00000000
#> 2  1.84029996 -0.2366167  1  0    -0.7956697      0.000000   -0.06067571
#> 3 -1.47564138  1.1240482  1  0     1.1767483      0.000000    0.59285327
#> 4  0.30656699 -0.3398171  0  1     0.0000000     -2.724644    0.00000000
#> 5  0.03947981  1.5644167  1  0     1.1035159      0.000000   -0.83287567
#> 6 -0.80024527  1.0026215  0  1     0.0000000      2.800868    0.00000000
#>   int_M2diff_W2 int_M1avg_W1 int_M1avg_W2 int_M2avg_W1 int_M2avg_W2
#> 1      1.070554    0.0000000   -0.2230613    0.0000000  -0.04728171
#> 2      0.000000   -0.3230789    0.0000000    0.0495861   0.00000000
#> 3      0.000000   -0.3561418    0.0000000   -0.5766869   0.00000000
#> 4     -1.087391    0.0000000    0.1240715    0.0000000  -0.34785861
#> 5      0.000000    0.7723784    0.0000000   -1.0332144   0.00000000
#> 6     -2.842480    0.0000000    0.4710784    0.0000000   0.38385556