wsMed (version 1.0.2)

PrepareMissingData: 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
)

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.

Arguments

data_missing

A data frame containing the raw dataset with missing values.

m

An integer specifying the number of imputations to perform. Default is 5.

method_num

Character; imputation method for numeric variables (for example, "pmm", "norm"). Default is "pmm".

seed

An integer specifying the random seed for reproducibility. Default is 123.

M_C1

A character vector of column names representing mediators at condition 1.

M_C2

A character vector of column names representing mediators at condition 2. Must match the length of M_C1.

Y_C1

A character string representing the column name of the outcome variable at condition 1.

Y_C2

A character string representing the column name of the outcome variable at condition 2.

C_C1

Character vector of within-subject control variable names (condition 1).

C_C2

Character vector of within-subject control variable names (condition 2).

C

Character vector of between-subject control variable names.

C_type

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

W

Optional character vector: moderator names (at most J).

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; keep the original W / C columns in the returned data?

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.

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