el_survey: Empirical likelihood for survey designs (NMAR)

Description

Internal method dispatched by el() when data is a survey.design.

Usage

# S3 method for survey.design
el(
  data,
  formula,
  auxiliary_means = NULL,
  standardize = TRUE,
  strata_augmentation = 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(),
  ...
)

Value

c("nmar_result_el","nmar_result").

Arguments

data: A survey.design created with survey::svydesign().
formula: Two-sided formula with an NA-valued outcome on the LHS; auxiliaries on the first RHS and, optionally, missingness predictors on the second RHS partition.
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; standardize predictors.
strata_augmentation: Logical; when TRUE (default), augment the auxiliary design with stratum indicators and stratum shares when a strata structure is present in the survey design.
trim_cap: Numeric; cap for EL weights (Inf = no trimming).
control: List; solver control for nleqslv::nleqslv(control = ...).
on_failure: Character; "return" or "error" on solver failure.
variance_method: Character; "bootstrap" or "none".
bootstrap_reps: Integer; reps when variance_method = "bootstrap".
n_total: Optional analysis-scale population size N_pop; required for respondents-only designs.
start: Optional list of starting values passed to solver helpers.
trace_level: Integer 0-3 controlling estimator logging detail.
family: Missingness (response) model family specification (defaults to logit).
...: Passed to solver.

Details

Implements the empirical likelihood estimator with design weights. If n_total is supplied, it is treated as the analysis-scale population size N_pop used in the design-weighted QLS system. If n_total is not supplied, sum(weights(design)) is used as N_pop. Design weights are not rescaled internally; the EL equations use respondent weights and N_pop via \(T_0 = N_{\mathrm{pop}} - \sum d_i\) in the linkage equation. When respondents-only designs are used (no NA in the outcome), n_total must be provided; if auxiliaries are requested you must also provide population auxiliary means via auxiliary_means. Result weights are the unnormalized EL masses \(d_i / D_i(\theta)\) on this analysis scale; weights(result, scale = "population") sums to N_pop.