Add inactive replicates to a survey design object — add_inactive

Adds inactive replicates to a survey design object. An inactive replicate is a replicate that does not contribute to variance estimates but adds to the matrix of replicate weights so that the matrix has the desired number of columns. The new replicates' values are simply equal to the full-sample weights.

Usage

add_inactive_replicates(design, n_total, n_to_add, location = "last")

Arguments

design: A survey design object, created with either the survey or srvyr packages.
n_total: The total number of replicates that the result should contain. If the design already contains n_total replicates (or more), then no update is made.
n_to_add: The number of additional replicates to add. Can only use the n_total argument OR the n_to_add argument, not both.
location: Either "first", "last" (the default), or "random". Specifies where the columns of new replicates should be located in the matrix of replicate weights. Use "first" to place new replicates first (i.e., in the leftmost part of the matrix), "last" to place the new replicates last (i.e., in the rightmost part of the matrix). Use "random" to intersperse the new replicates in random column locations of the matrix; the original replicates will still be in their original order.

Value

An updated survey design object, where the number of columns of replicate weights has potentially increased. The increase only happens if the user specifies the n_to_add argument instead of n_total, of if the user specifies n_total and n_total is less than the number of columns of replicate weights that the design already had.

Statistical Details

Inactive replicates are also sometimes referred to as "dead replicates", for example in Ash (2014). The purpose of adding inactive replicates is to increase the number of columns of replicate weights without impacting variance estimates. This can be useful, for example, when combining data from a survey across multiple years, where different years use different number of replicates, but a consistent number of replicates is desired in the combined data file.

Suppose the initial replicate design has $L$ replicates, with respective constants $c_k$ for $k=1,\dots,L$ used to estimate variance with the formula $$v_{R} = \sum_{k=1}^L c_k\left(\hat{T}_y^{(k)}-\hat{T}_y\right)^2$$ where $\hat{T}_y$ is the estimate produced using the full-sample weights and $\hat{T}_y^{(k)}$ is the estimate from replicate $k$.

Inactive replicates are simply replicates that are exactly equal to the full sample: that is, the replicate $k$ is called "inactive" if its vector of replicate weights exactly equals the full-sample weights. In this case, when using the formula above to estimate variances, these replicates contribute nothing to the variance estimate.

If the analyst uses the variant of the formula above where the full-sample estimate $\hat{T}_y$ is replaced by the average replicate estimate (i.e., $L^{-1}\sum_{k=1}^{L}\hat{T}_y^{(k)}$), then variance estimates will differ before vs. after adding the inactive replicates. For this reason, it is strongly recommend to explicitly specify mse=TRUE when creating a replicate design object in R with functions such as svrepdesign(), as_bootstrap_design(), etc. If working with an already existing replicate design, you can update the mse option to TRUE simply by using code such as my_design$mse <- TRUE.

References

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

Examples

library(survey)
#> Loading required package: grid
#> Loading required package: Matrix
#> Loading required package: survival
#> 
#> Attaching package: ‘survey’
#> The following object is masked from ‘package:graphics’:
#> 
#>     dotchart
set.seed(2023)

# Create an example survey design object

  sample_data <- data.frame(
    PSU     = c(1,2,3)
  )

  survey_design <- svydesign(
    data = sample_data,
    ids = ~ PSU,
    weights = ~ 1
  )

  rep_design <- survey_design |>
    as.svrepdesign(type = "JK1", mse = TRUE)

# Inspect replicates before subsampling

  rep_design |> weights(type = "analysis")
#>      [,1] [,2] [,3]
#> [1,]  0.0  1.5  1.5
#> [2,]  1.5  0.0  1.5
#> [3,]  1.5  1.5  0.0

# Inspect replicates after adding inactive replicates

  rep_design |>
    add_inactive_replicates(n_total = 5, location = "first") |>
    weights(type = "analysis")
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    1  0.0  1.5  1.5
#> [2,]    1    1  1.5  0.0  1.5
#> [3,]    1    1  1.5  1.5  0.0

  rep_design |>
    add_inactive_replicates(n_to_add = 2, location = "last") |>
    weights(type = "analysis")
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]  0.0  1.5  1.5    1    1
#> [2,]  1.5  0.0  1.5    1    1
#> [3,]  1.5  1.5  0.0    1    1

  rep_design |>
    add_inactive_replicates(n_to_add = 5, location = "random") |>
    weights(type = "analysis")
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#> [1,]    1    1    1  0.0    1  1.5    1  1.5
#> [2,]    1    1    1  1.5    1  0.0    1  1.5
#> [3,]    1    1    1  1.5    1  1.5    1  0.0