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Impute missing counts under Missing at Random (MAR)

Source: R/multiple_imputation_nb.R
impute_nb_mar.Rd

For observations whose missingness flag matches mar_values, generates n_imp imputed counts using the GLMM predicted mean including subject-level BLUPs. Draws use the Gamma–Poisson compound distribution: $$\lambda_i \sim \text{Gamma}(1/k,\; \hat\mu_i^{\text{BLUP}} k), \quad Y_i^{(m)} \mid \lambda_i \sim \text{Poisson}(\lambda_i).$$

Usage

impute_nb_mar(
  data,
  fits,
  outcome_col,
  miss_flag_col,
  mar_values = "MAR",
  n_imp = 5L,
  replicate_col = NULL
)

Arguments

data

Data frame including all rows (observed and missing).

fits

Named list of fits as returned by fit_nb_glmm().

outcome_col

Character. Column with the count outcome (may have NA).

miss_flag_col

Character. Column with the missingness flag.

mar_values

Character vector. Flag values treated as MAR. Default "MAR". In the reference-based framework, reference-arm MNAR subjects are also imputed as MAR (pass c("MAR", "MNAR") if desired).

n_imp

Integer. Number of imputations per replicate. Default 5.

replicate_col

Character or NULL. Replicate identifier column.

Value

Data frame in long format with all original columns plus imputation (integer 1 to n_imp) and imputed_value (imputed count; equals the observed value for non-missing rows).

Details

This function handles one or more bootstrap replicates when a replicate_col is provided and fits contains one model per replicate. Observed rows (non-missing outcome) are passed through unchanged (imputed_value = observed value).

Examples

if (FALSE) { # \dontrun{
fits    <- fit_nb_glmm(obs_data, count ~ base + trt + visit + (1 | id))
imp_mar <- impute_nb_mar(
  data          = long_data,
  fits          = fits,
  outcome_col   = "count",
  miss_flag_col = "miss_flag",
  mar_values    = "MAR",
  n_imp         = 5L
)
} # }