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Convert a survey design object to a random-groups jackknife design

Source: R/as_jackknife_design.R
as_random_group_jackknife_design.Rd

Forms a specified number of jackknife replicates based on grouping primary sampling units (PSUs) into random, (approximately) equal-sized groups.

Usage

as_random_group_jackknife_design(
  design,
  replicates = 50,
  var_strat = NULL,
  var_strat_frac = NULL,
  sort_var = NULL,
  adj_method = "variance-stratum-psus",
  scale_method = "variance-stratum-psus",
  group_var_name = ".random_group",
  compress = TRUE,
  mse = getOption("survey.replicates.mse")
)

Arguments

design

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

replicates

The number of replicates to create for each variance stratum. The total number of replicates created is the number of variance strata times replicates. Every design stratum must have at least as many primary sampling units (PSUs), as replicates.

var_strat

Specifies the name of a variable in the data that defines variance strata to use for the grouped jackknife. If var_strat = NULL, then there is effectively only one variance stratum.

var_strat_frac

Specifies the sampling fraction to use for finite population corrections in each value of var_strat. Can use either a single number or a variable in the data corresponding to var_strat.

sort_var

(Optional) Specifies the name of a variable in the data which should be used to sort the data before assigning random groups. If a variable is specified for var_strat, the sorting will happen within values of that variable.

adj_method

Specifies how to calculate the replicate weight adjustment factor. Available options for adj_method include:

  • "variance-stratum-psus" (the default)
    The replicate weight adjustment for a unit is based on the number of PSUs in its variance stratum.

  • "variance-units"
    The replicate weight adjustment for a unit is based on the number of variance units in its variance stratum.

See the section "Adjustment and Scale Methods" for details.

scale_method

Specifies how to calculate the scale factor for each replicate. Available options for scale_method include:

  • "variance-stratum-psus"
    The scale factor for a variance unit is based on its number of PSUs compared to the number of PSUs in its variance stratum.

  • "variance-units"
    The scale factor for a variance unit is based on the number of variance units in its variance stratum.

See the section "Adjustment and Scale Methods" for details.

group_var_name

(Optional) The name of a new variable created to save identifiers for which random group each PSU was grouped into for the purpose of forming replicates. Specify group_var_name = NULL to avoid creating the variable in the data.

compress

Use a compressed representation of the replicate weights matrix. This reduces the computer memory required to represent the replicate weights and has no impact on estimates.

mse

If TRUE, 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.

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.

Formation of Random Groups

Within each value of VAR_STRAT, the data are sorted by first-stage sampling strata, and then the PSUs in each stratum are randomly arranged. Groups are then formed by serially placing PSUs into each group. The first PSU in the VAR_STRAT is placed into the first group, the second PSU into the second group, and so on. Once a PSU has been assigned to the last group, the process begins again by assigning the next PSU to the first group, the PSU after that to the second group, and so on.

The random group that each observation is assigned to can be saved as a variable in the data by using the function argument group_var_name.

Adjustment and Scale Methods

The jackknife replication variance estimator based on \(R\) replicates takes the following form: $$ v(\hat{\theta}) = \sum_{r=1}^{R} (1 - f_r) \times c_r \times \left(\hat{\theta}_r - \hat{\theta}\right)^2 $$ where \(r\) indexes one of the \(R\) sets of replicate weights, \(c_r\) is a corresponding scale factor for the \(r\)-th replicate, and \(1 - f_r\) is an optional finite population correction factor that can potentially differ across variance strata.

To form the replicate weights, the PSUs are divided into \(\tilde{H}\) variance strata, and the \(\tilde{h}\)-th variance stratum contains \(G_{\tilde{h}}\) random groups. The number of replicates \(R\) equals the total number of random groups across all variance strata: \(R = \sum_{\tilde{h}}^{\tilde{H}} G_{\tilde{h}}\). In other words, each replicate corresponds to one of the random groups from one of the variance strata.

The weights for replicate \(r\) corresponding to random group \(g\) within variance stratum \(\tilde{h}\) is defined as follows.

If case \(i\) is not in variance stratum \(\tilde{h}\), then \(w_{i}^{(r)} = w_i\).

If case \(i\) is in variance stratum \(\tilde{h}\) but in random group \(g\), then \(w_{i}^{(r)} = a_{\tilde{h}g} w_i\).

Otherwise, if case \(i\) is in in random group \(g\) of variance stratum \(\tilde{h}\), then \(w_{i}^{(r)} = 0\).

The R function argument adj_method determines how the adjustment factor \(a_{\tilde{h} g}\) is calculated. When adj_method = "variance-units", then \(a_{\tilde{h} g}\) is calculated based on \(G_{\tilde{h}}\), which is the number of random groups in variance stratum \(\tilde{h}\). When adj_method = "variance-stratum-psus", then \(a_{\tilde{h} g}\) is calculated based on \(n_{\tilde{h}g}\), which is the number of PSUs in random group \(g\) in variance stratum \(\tilde{h}\), as well as \(n_{\tilde{h}}\), the total number of PSUs in variance stratum \(\tilde{h}\).

If adj_method = "variance-units", then: $$a_{\tilde{h}g} = \frac{G_{\tilde{h}}}{G_{\tilde{h}} - 1}$$

If adj_method = "variance-stratum-psus", then: $$a_{\tilde{h}g} = \frac{n_{\tilde{h}}}{n_{\tilde{h}} - n_{\tilde{h}g}}$$

The scale factor \(c_r\) for replicate \(r\) corresponding to random group \(g\) within variance stratum \(\tilde{h}\) is calculated according to the function argument scale_method.

If scale_method = "variance-units", then: $$c_r = \frac{G_{\tilde{h}} - 1}{G_{\tilde{h}}}$$

If scale_method = "variance-stratum-psus", then: $$c_r = \frac{n_{\tilde{h}} - n_{\tilde{h}g}}{n_{\tilde{h}}}$$

The sampling fraction \(f_r\) used for finite population correction \(1 - f_r\) is by default assumed to equal 0. However, the user can supply a sampling fraction for each variance stratum using the argument var_strat_frac.

When variance units in a variance stratum have differing numbers of PSUs, the combination adj_method = "variance-stratum-psus" and scale_method = "variance-units" is recommended by Valliant, Brick, and Dever (2008), corresponding to their method "GJ2".

The random-groups jackknife method often referred to as "DAGJK" corresponds to the options var_strat = NULL, adj_method = "variance-units", and scale_method = "variance-units". The DAGJK method will yield upwardly-biased variance estimates for totals if the total number of PSUs is not a multiple of the total number of replicates (Valliant, Brick, and Dever 2008).

References

See Section 15.5 of Valliant, Dever, and Kreuter (2018) for an introduction to the grouped jackknife and guidelines for creating the random groups.

- Valliant, R., Dever, J., Kreuter, F. (2018). "Practical Tools for Designing and Weighting Survey Samples, 2nd edition." New York: Springer.

See Valliant, Brick, and Dever (2008) for statistical details related to the adj_method and scale_method arguments.

- Valliant, Richard, Michael Brick, and Jill Dever. 2008. "Weight Adjustments for the Grouped Jackknife Variance Estimator." Journal of Official Statistics. 24: 469–88.

See Chapter 4 of Wolter (2007) for additional details of the jackknife, including the method based on random groups.

- Wolter, Kirk. 2007. "Introduction to Variance Estimation." New York, NY: Springer New York. https://doi.org/10.1007/978-0-387-35099-8.

Examples

library(survey)

# Load example data

 data('api', package = 'survey')

 api_strat_design <- svydesign(
   data = apistrat,
   id = ~ 1,
   strata = ~stype,
   weights = ~pw
 )

# Create a random-groups jackknife design

 jk_design <- as_random_group_jackknife_design(
   api_strat_design,
   replicates = 15
 )
 print(jk_design)
#> Call: as_random_group_jackknife_design(api_strat_design, replicates = 15)
#> with 15 replicates.