as_fays_gen_rep_design {svrep}

Convert a survey design object to a replication design using Fay's generalized replication method

Description

Converts a survey design object to a replicate design object with replicate weights formed using the generalized replication method of Fay (1989). The generalized replication method forms replicate weights from a textbook variance estimator, provided that the variance estimator can be represented as a quadratic form whose matrix is positive semidefinite (this covers a large class of variance estimators).

Usage

as_fays_gen_rep_design(
  design,
  variance_estimator = NULL,
  aux_var_names = NULL,
  max_replicates = 500,
  balanced = TRUE,
  psd_option = "warn",
  mse = TRUE,
  compress = TRUE
)

Arguments

design

A survey design object created using the 'survey' (or 'srvyr') package, with class 'survey.design' or 'svyimputationList'.

variance_estimator

The name of the variance estimator whose quadratic form matrix should be created. See variance-estimators for a detailed description of each variance estimator. Options include:

• "Yates-Grundy":
  The Yates-Grundy variance estimator based on first-order and second-order inclusion probabilities.

• "Horvitz-Thompson":
  The Horvitz-Thompson variance estimator based on first-order and second-order inclusion probabilities.

• "Poisson Horvitz-Thompson":
  The Horvitz-Thompson variance estimator based on assuming Poisson sampling, with first-order inclusion probabilities inferred from the sampling probabilities of the survey design object.

• "Stratified Multistage SRS":
  The usual stratified multistage variance estimator based on estimating the variance of cluster totals within strata at each stage.

• "Ultimate Cluster":
  The usual variance estimator based on estimating the variance of first-stage cluster totals within first-stage strata.

• "Deville-1":
  A variance estimator for unequal-probability sampling without replacement, described in Matei and Tillé (2005) as "Deville 1".

• "Deville-2":
  A variance estimator for unequal-probability sampling without replacement, described in Matei and Tillé (2005) as "Deville 2".

• "Deville-Tille":
  A variance estimator useful for balanced sampling designs, proposed by Deville and Tillé (2005).

• "SD1":
  The non-circular successive-differences variance estimator described by Ash (2014), sometimes used for variance estimation for systematic sampling.

• "SD2":
  The circular successive-differences variance estimator described by Ash (2014). This estimator is the basis of the "successive-differences replication" estimator commonly used for variance estimation for systematic sampling.

aux_var_names

(Only used if variance_estimator = "Deville-Tille"). A vector of the names of auxiliary variables used in sampling.

max_replicates

The maximum number of replicates to allow (should be as large as possible, given computer memory/storage limitations). A commonly-recommended default is 500. If the number of replicates needed for a balanced, fully-efficient estimator is less than max_replicates, then only the number of replicates needed will be created. If more replicates are needed than max_replicates, then the full number of replicates needed will be created, but only a random subsample will be retained.

balanced

If balanced=TRUE, the replicates will all contribute equally to variance estimates, but the number of replicates needed may slightly increase.

psd_option

Either "warn" (the default) or "error". This option specifies what will happen if the target variance estimator has a quadratic form matrix which is not positive semidefinite. This can occasionally happen, particularly for two-phase designs.
If psd_option="error", then an error message will be displayed.
If psd_option="warn", then a warning message will be displayed, and the quadratic form matrix will be approximated by the most similar positive semidefinite matrix. This approximation was suggested by Beaumont and Patak (2012), who note that this is conservative in the sense of producing overestimates of variance. Beaumont and Patak (2012) argue that this overestimation is expected to be small in magnitude. See get_nearest_psd_matrix for details of the approximation.

mse

If TRUE (the default), compute variances from sums of squares around the point estimate from the full-sample weights, If FALSE, compute variances from sums of squares around the mean estimate from the replicate weights. For Fay's generalized replication method, setting mse = FALSE can potentially lead to large underestimates of variance.

compress

This reduces the computer memory required to represent the replicate weights and has no impact on estimates.

Value

A replicate design object, with class svyrep.design, which can be used with the usual functions, such as svymean() or svyglm().

Use weights(..., type = 'analysis') to extract the matrix of replicate weights.

Use as_data_frame_with_weights() to convert the design object to a data frame with columns for the full-sample and replicate weights.

Statistical Details

See Fay (1989) for a full description of this replication method, or see the documentation in make_fays_gen_rep_factors for implementation details.

See variance-estimators for a description of each variance estimator available for use with this function.

Use rescale_reps to eliminate negative adjustment factors.

Two-Phase Designs

For a two-phase design, variance_estimator should be a list of variance estimators' names, with two elements, such as list('Ultimate Cluster', 'Poisson Horvitz-Thompson'). In two-phase designs, only the following estimators may be used for the second phase:

• "Ultimate Cluster"

• "Stratified Multistage SRS"

• "Poisson Horvitz-Thompson"

For statistical details on the handling of two-phase designs, see the documentation for make_twophase_quad_form.

References

The generalized replication method was first proposed in Fay (1984). Fay (1989) refined the generalized replication method to produce "balanced" replicates, in the sense that each replicate contributes equally to variance estimates. The advantage of balanced replicates is that one can still obtain a reasonable variance estimate by using only a random subset of the replicates.

- Ash, S. (2014). "Using successive difference replication for estimating variances." Survey Methodology, Statistics Canada, 40(1), 47–59.

- Deville, J.‐C., and Tillé, Y. (2005). "Variance approximation under balanced sampling." Journal of Statistical Planning and Inference, 128, 569–591.

- Dippo, Cathryn, Robert Fay, and David Morganstein. 1984. “Computing Variances from Complex Samples with Replicate Weights.” In, 489–94. Alexandria, VA: American Statistical Association. http://www.asasrms.org/Proceedings/papers/1984_094.pdf.

- Fay, Robert. 1984. “Some Properties of Estimates of Variance Based on Replication Methods.” In, 495–500. Alexandria, VA: American Statistical Association. http://www.asasrms.org/Proceedings/papers/1984_095.pdf.

- Fay, Robert. 1989. “Theory And Application Of Replicate Weighting For Variance Calculations.” In, 495–500. Alexandria, VA: American Statistical Association. http://www.asasrms.org/Proceedings/papers/1989_033.pdf

- Matei, Alina, and Yves Tillé. (2005). “Evaluation of Variance Approximations and Estimators in Maximum Entropy Sampling with Unequal Probability and Fixed Sample Size.” Journal of Official Statistics, 21(4):543–70.

See Also

For greater customization of the method, make_quad_form_matrix can be used to represent several common variance estimators as a quadratic form's matrix, which can then be used as an input to make_fays_gen_rep_factors.

Examples

if (FALSE) {

  library(survey)

  ## Load an example systematic sample ----
  data('library_stsys_sample', package = 'svrep')

  ## First, ensure data are sorted in same order as was used in sampling
  library_stsys_sample <- library_stsys_sample[
    order(library_stsys_sample$SAMPLING_SORT_ORDER),
  ]

  ## Create a survey design object
  design_obj <- svydesign(
    data = library_stsys_sample,
    strata = ~ SAMPLING_STRATUM,
    ids = ~ 1,
    fpc = ~ STRATUM_POP_SIZE
  )

  ## Convert to generalized replicate design

  gen_rep_design_sd2 <- as_fays_gen_rep_design(
    design = design_obj,
    variance_estimator = "SD2",
    max_replicates = 250,
    mse = TRUE
  )

  svytotal(x = ~ TOTSTAFF, na.rm = TRUE, design = gen_rep_design_sd2)
}