Impute Missing Values Using Carry Forward in Longitudinal Data — margot_impute_carry

Imputes missing values in longitudinal data by carrying forward previous observations up to a specified number of time points back. Optionally, it can create indicator variables for the imputed values.

Usage

margot_impute_carry_forward(
  df_wide,
  columns_to_impute,
  max_carry_forward = 1,
  time_point_prefixes = NULL,
  time_point_regex = NULL,
  require_one_observed = TRUE,
  columns_no_future_required = NULL,
  create_na_indicator = TRUE,
  indicator_suffix = "_na",
  indicator_as_suffix = TRUE,
  verbose = TRUE
)

Arguments

df_wide: A wide-format dataframe containing longitudinal data.
columns_to_impute: Character vector of base column names to impute (without time prefixes).
max_carry_forward: Maximum number of time points to look back for carrying forward values.
time_point_prefixes: Optional vector of time point prefixes (e.g., c("t0", "t1", "t2")).
time_point_regex: Optional regex pattern to identify time points. Overrides time_point_prefixes if provided.
require_one_observed: Logical. If TRUE, only impute if at least one value is observed in the next wave.
columns_no_future_required: Character vector of columns that do not require future observations for imputation. Defaults to all columns if require_one_observed = FALSE, or none if require_one_observed = TRUE.
create_na_indicator: Logical. If TRUE, creates indicator variables for imputed values.
indicator_suffix: Suffix to add to the original column name for the indicator variable (default is "_na").
indicator_as_suffix: Logical. If TRUE, the indicator suffix is added as a suffix; if FALSE, it's added as a prefix.
verbose: Logical. If TRUE, prints progress information.

Value

A dataframe with imputed values and optional indicator variables.

Examples

# [Examples as provided in the original function]