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Prepare EL inputs for IID and survey designs

Source: R/engines_el_impl_input.R
el_prepare_inputs.Rd

Parses the two-part Formula, constructs EL design matrices, injects the respondent delta indicator, attaches weights and (optionally) survey metadata, and returns the pieces needed by the EL core. The outcome enters the missingness design only through the evaluated LHS expression; any explicit use of the outcome variable on RHS2 is rejected.

Usage

el_prepare_inputs(
  formula,
  data,
  weights = NULL,
  n_total = NULL,
  design_object = NULL
)

Details

Invariants enforced here (relied on by all downstream EL code):

  • LHS references exactly one outcome source variable in data; any transforms are applied via the formula environment and must be defined for all respondent rows.

  • The outcome is never allowed to appear on RHS1 (auxiliaries) or RHS2 (missingness predictors), either explicitly in the formula or implicitly via dot (.) expansion. The missingness model uses the evaluated LHS expression as a dedicated predictor column instead.

  • RHS1 always yields an intercept-free auxiliary design matrix with k-1 coding for factor auxiliaries, regardless of user +0/-1 syntax or custom contrasts. Auxiliary columns are validated to be fully observed and non-constant among respondents.

  • RHS2 always yields a missingness-design matrix for respondents that includes an intercept column and zero-variance predictors only emit warnings (not errors); NA among respondents is rejected.

  • respondent_mask is defined from the raw outcome in data, not from the transformed LHS; an injected ..nmar_delta.. indicator in analysis_data matches this mask exactly.

  • N_pop is the analysis-scale population size used in the EL system: for IID it is nrow(data) unless overridden by n_total; for survey designs it is sum(weights) or n_total when supplied.