el_dataframe: Empirical likelihood for data frames (NMAR)

Description

Internal method dispatched by el() when data is a data.frame. Returns c("nmar_result_el","nmar_result") with the point estimate, optional bootstrap SE, weights, coefficients, diagnostics, and metadata.

Usage

# S3 method for data.frame
el(
  data,
  formula,
  auxiliary_means = NULL,
  standardize = TRUE,
  trim_cap = Inf,
  control = list(),
  on_failure = c("return", "error"),
  variance_method = c("bootstrap", "none"),
  bootstrap_reps = 500,
  n_total = NULL,
  start = NULL,
  trace_level = 0,
  family = logit_family(),
  ...
)

Arguments

data: A data.frame where the outcome column contains NA for nonrespondents.
formula: Two-sided formula Y_miss ~ auxiliaries or Y_miss ~ auxiliaries | missingness_predictors.
auxiliary_means: Named numeric vector of population means for auxiliary design columns. Names must match the materialized model.matrix columns on the first RHS (after formula expansion), including factor indicators and transformed terms. The intercept is always excluded.
standardize: Logical; whether to standardize predictors prior to estimation.
trim_cap: Numeric; cap for EL weights (Inf = no trimming).
control: List; optional solver control parameters for nleqslv::nleqslv(control = ...).
on_failure: Character; one of "return" or "error" on solver failure.
variance_method: Character; one of "bootstrap" or "none".
bootstrap_reps: Integer; number of bootstrap reps if variance_method = "bootstrap".
n_total: Optional analysis-scale population total N_pop. When the outcome contains at least one NA, n_total defaults to nrow(data). When respondents-only data are supplied (no NA in the outcome), n_total must be provided.
start: Optional list of starting values passed to the solver helpers.
trace_level: Integer 0-3 controlling estimator logging detail.
family: Missingness (response) model family specification (defaults to the logit bundle).
...: Additional arguments passed to the solver.

Details

Implements the empirical likelihood estimator for IID data with optional auxiliary moment constraints. The missingness-model score is the Bernoulli derivative with respect to the linear predictor, supporting logit and probit links. When respondents-only data are supplied (no NA in the outcome), n_total is required so the response-rate equation targets the full sample size. When missingness is observed (NA present), the default population total is nrow(data). If respondents-only data are used and auxiliaries are requested, you must also provide population auxiliary means via auxiliary_means. Result weights are the unnormalized EL masses \(a_i / D_i(\theta)\) on the analysis scale, where \(a_i \equiv 1\) for IID data.