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Classify Parameters by Design Sensitivity

Source: R/der_classify.R
der_classify.Rd

Assigns each estimable parameter to a design-sensitivity tier and flags those whose DER exceeds the threshold tau. The estimable-parameter classification:

  • Tier I-a (fe_within): Survey-dominated parameters.

  • Tier I-b (fe_between): Protected between-cluster parameters.

  • Tier II (re): Protected random effects.

Usage

der_classify(x, tau = 1.2, verbose = TRUE)

Arguments

x

A svyder object from der_compute().

tau

Threshold (default 1.2). Parameters with DER > tau are flagged.

verbose

Print classification summary (default TRUE).

Value

A svyder object with updated classification and tau fields.

Details

Parameters with DER > tau (strict inequality) are flagged for correction, regardless of tier.

Tier DER signatures

Each tier has a closed-form DER signature under the decomposition theorems (with \(B\) the shrinkage factor, \(J\) the number of groups):

  • Tier I-a (within-group fixed effects): \(\mathrm{DER} = \mathrm{DEFF}\) – fully exposed to the design and the primary correction candidates.

  • Tier I-b (between-group fixed effects): \(\mathrm{DER} = \mathrm{DEFF}\,(1 - B)\) – shielded by shrinkage and usually below the threshold.

  • Tier II (random effects): \(\mathrm{DER} = B\cdot\mathrm{DEFF}\cdot\kappa(J)\), with the finite-group correction \(\kappa(J) = 1 - 1/[J(1-B)+B]\).

  • Tier III (hyperparameters, e.g. \(\sigma_\theta\)): excluded by construction because they are outside the declared location-block DER target. Reported in $excluded, never flagged.

References

Lee, J., Williams, M. R., & Savitsky, T. D. (2026). Design Effect Ratios for Bayesian Survey Models: A Diagnostic Framework for Identifying Survey-Sensitive Parameters. Journal of Survey Statistics and Methodology. Submitted.

See also

der_compute() for computing DER values, der_correct() for applying corrections, der_diagnose() for the all-in-one pipeline.

Other core-pipeline: der_compute(), der_correct(), der_diagnose()

Examples

data(nsece_demo)
result <- der_compute(
  nsece_demo$draws,
  y = nsece_demo$y, X = nsece_demo$X,
  group = nsece_demo$group, weights = nsece_demo$weights,
  cluster = nsece_demo$psu, family = "binomial",
  sigma_theta = nsece_demo$sigma_theta,
  param_types = nsece_demo$param_types
)
result <- der_classify(result, tau = 1.2)
#> DER Classification (tau = 1.20)
#>   Total parameters: 54
#>   Tier III excluded (DER undefined): sigma_theta
#>   Flagged: 30 (55.6%)
#>   Flagged parameters:
#>     beta[2]: DER = 2.689 [I-a] -> CORRECT
#>     theta[1]: DER = 3.386 [II] -> CORRECT
#>     theta[4]: DER = 2.210 [II] -> CORRECT
#>     theta[5]: DER = 1.568 [II] -> CORRECT
#>     theta[6]: DER = 2.104 [II] -> CORRECT
#>     theta[7]: DER = 2.232 [II] -> CORRECT
#>     theta[9]: DER = 5.308 [II] -> CORRECT
#>     theta[11]: DER = 2.655 [II] -> CORRECT
#>     theta[15]: DER = 1.576 [II] -> CORRECT
#>     theta[18]: DER = 4.025 [II] -> CORRECT
#>     theta[19]: DER = 1.992 [II] -> CORRECT
#>     theta[20]: DER = 2.469 [II] -> CORRECT
#>     theta[21]: DER = 1.790 [II] -> CORRECT
#>     theta[23]: DER = 1.311 [II] -> CORRECT
#>     theta[27]: DER = 2.324 [II] -> CORRECT
#>     theta[29]: DER = 1.631 [II] -> CORRECT
#>     theta[30]: DER = 2.968 [II] -> CORRECT
#>     theta[31]: DER = 1.288 [II] -> CORRECT
#>     theta[32]: DER = 1.715 [II] -> CORRECT
#>     theta[34]: DER = 2.844 [II] -> CORRECT
#>     theta[35]: DER = 1.258 [II] -> CORRECT
#>     theta[36]: DER = 2.280 [II] -> CORRECT
#>     theta[39]: DER = 2.222 [II] -> CORRECT
#>     theta[40]: DER = 1.567 [II] -> CORRECT
#>     theta[41]: DER = 1.821 [II] -> CORRECT
#>     theta[43]: DER = 2.301 [II] -> CORRECT
#>     theta[44]: DER = 3.217 [II] -> CORRECT
#>     theta[45]: DER = 1.505 [II] -> CORRECT
#>     theta[46]: DER = 1.225 [II] -> CORRECT
#>     theta[47]: DER = 2.134 [II] -> CORRECT