make_quad_form_matrix {svrep}

Represent a variance estimator as a quadratic form

Description

Common variance estimators for estimated population totals can be represented as a quadratic form. Given a choice of variance estimator and information about the sample design, this function constructs the matrix of the quadratic form.

In notation, let v(\hat{Y}) = \mathbf{\breve{y}}^{\prime}\mathbf{\Sigma}\mathbf{\breve{y}}, where \breve{y} is the vector of weighted values, y_i/\pi_i, \space i=1,\dots,n. This function constructs the n \times n matrix of the quadratic form, \mathbf{\Sigma}.

Usage

make_quad_form_matrix(
  variance_estimator = "Yates-Grundy",
  probs = NULL,
  joint_probs = NULL,
  cluster_ids = NULL,
  strata_ids = NULL,
  strata_pop_sizes = NULL,
  sort_order = NULL,
  aux_vars = NULL
)

Arguments

variance_estimator

The name of the variance estimator whose quadratic form matrix should be created. See the section "Variance Estimators" below. Options include:

• "Yates-Grundy": The Yates-Grundy variance estimator based on first-order and second-order inclusion probabilities. If this is used, the argument joint_probs must also be used.

• "Horvitz-Thompson": The Horvitz-Thompson variance estimator based on first-order and second-order inclusion probabilities. If this is used, the argument joint_probs must also be used.

• "Stratified Multistage SRS": The usual stratified multistage variance estimator based on estimating the variance of cluster totals within strata at each stage. If this option is used, then it is necessary to also use the arguments strata_ids, cluster_ids, strata_pop_sizes, and strata_pop_sizes.

• "Ultimate Cluster": The usual variance estimator based on estimating the variance of first-stage cluster totals within first-stage strata. If this option is used, then it is necessary to also use the arguments strata_ids, cluster_ids, strata_pop_sizes. Optionally, to use finite population correction factors, one can also use the argument strata_pop_sizes.

• "Deville-1": A variance estimator for unequal-probability sampling without replacement, described in Matei and Tillé (2005) as "Deville 1". If this option is used, then it is necessary to also use the arguments strata_ids, cluster_ids, and probs.

• "Deville-2": A variance estimator for unequal-probability sampling without replacement, described in Matei and Tillé (2005) as "Deville 2". If this option is used, then it is necessary to also use the arguments strata_ids, cluster_ids, and probs.

• "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.

• "Deville-Tille": The estimator of Deville and Tillé (2005), developed for balanced sampling using the cube method.

probs

Required if variance_estimator equals "Deville-1", "Deville-2", or "Breidt-Chauvet". This should be a matrix or data frame of sampling probabilities. If there are multiple stages of sampling, then probs can have multiple columns, with one column for each level of sampling to be accounted for by the variance estimator.

joint_probs

Only used if variance_estimator = "Horvitz-Thompson" or variance_estimator = "Yates-Grundy". This should be a matrix of joint inclusion probabilities. Element [i,i] of the matrix is the first-order inclusion probability of unit i, while element [i,j] is the joint inclusion probability of units i and j.

cluster_ids

Required unless variance_estimator equals "Horvitz-Thompson" or "Yates-Grundy". This should be a matrix or data frame of cluster IDs. If there are multiple stages of sampling, then cluster_ids can have multiple columns, with one column for each level of sampling to be accounted for by the variance estimator.

strata_ids

Required if variance_estimator equals "Stratified Multistage SRS" or "Ultimate Cluster". This should be a matrix or data frame of strata IDs. If there are multiple stages of sampling, then strata_ids can have multiple columns, with one column for each level of sampling to be accounted for by the variance estimator.

strata_pop_sizes

Required if variance_estimator equals "Stratified Multistage SRS", but can optionally be used if variance_estimator equals "Ultimate Cluster", "SD1", or "SD2". If there are multiple stages of sampling, then strata_pop_sizes can have multiple columns, with one column for each level of sampling to be accounted for by the variance estimator.

sort_order

Required if variance_estimator equals "SD1" or "SD2". This should be a vector that orders the rows of data into the order used for sampling.

aux_vars

Required if variance_estimator equals "Deville-Tille". A matrix of auxiliary variables.

Value

The matrix of the quadratic form representing the variance estimator.

Variance Estimators

See variance-estimators for a description of each variance estimator.

Arguments required for each variance estimator

Below are the arguments that are required or optional for each variance estimator.

variance_estimator	probs	joint_probs	cluster_ids	strata_ids	strata_pop_sizes	sort_order	aux_vars
Stratified Multistage SRS			Required	Required	Required
Ultimate Cluster			Required	Required	Optional
SD1			Required	Optional	Optional	Required
SD2			Required	Optional	Optional	Required
Deville-1	Required		Required	Optional
Deville-2	Required		Required	Optional
Deville-Tille	Required		Required	Optional			Required
Yates-Grundy		Required
Horvitz-Thompson		Required

See Also

See variance-estimators for a description of each variance estimator.

For a two-phase design, the function make_twophase_quad_form combines the quadratic form matrix from each phase.

Examples

## Not run: 
# Example 1: The Horvitz-Thompson Estimator
  library(survey)
  data("election", package = "survey")

  ht_quad_form_matrix <- make_quad_form_matrix(variance_estimator = "Horvitz-Thompson",
                                               joint_probs = election_jointprob)
  ##_ Produce variance estimate
  wtd_y <- as.matrix(election_pps$wt * election_pps$Bush)
  t(wtd_y) %*% ht_quad_form_matrix %*% wtd_y

  ##_ Compare against result from 'survey' package
  svytotal(x = ~ Bush,
           design = svydesign(data=election_pps,
                              variance = "HT",
                              pps = ppsmat(election_jointprob),
                              ids = ~ 1, fpc = ~ p)) |> vcov()

# Example 2: Stratified multistage Sample ----

  data("mu284", package = 'survey')
  multistage_srswor_design <- svydesign(data = mu284,
                                        ids = ~ id1 + id2,
                                        fpc = ~ n1 + n2)

  multistage_srs_quad_form <- make_quad_form_matrix(
    variance_estimator = "Stratified Multistage SRS",
    cluster_ids = mu284[,c('id1', 'id2')],
    strata_ids = matrix(1, nrow = nrow(mu284), ncol = 2),
    strata_pop_sizes = mu284[,c('n1', 'n2')]
  )

  wtd_y <- as.matrix(weights(multistage_srswor_design) * mu284$y1)
  t(wtd_y) %*% multistage_srs_quad_form %*% wtd_y

  ##_ Compare against result from 'survey' package
  svytotal(x = ~ y1, design = multistage_srswor_design) |> vcov()

# Example 3: Successive-differences estimator ----

  data('library_stsys_sample', package = 'svrep')

  sd1_quad_form <- make_quad_form_matrix(
    variance_estimator = 'SD1',
    cluster_ids = library_stsys_sample[,'FSCSKEY',drop=FALSE],
    strata_ids = library_stsys_sample[,'SAMPLING_STRATUM',drop=FALSE],
    strata_pop_sizes = library_stsys_sample[,'STRATUM_POP_SIZE',drop=FALSE],
    sort_order = library_stsys_sample[['SAMPLING_SORT_ORDER']]
  )

  wtd_y <- as.matrix(library_stsys_sample[['TOTCIR']] /
                      library_stsys_sample$SAMPLING_PROB)
  wtd_y[is.na(wtd_y)] <- 0

  t(wtd_y) %*% sd1_quad_form %*% wtd_y

# Example 4: Deville estimators ----

 data('library_multistage_sample', package = 'svrep')

 deville_quad_form <- make_quad_form_matrix(
     variance_estimator = 'Deville-1',
     cluster_ids = library_multistage_sample[,c("PSU_ID", "SSU_ID")],
     strata_ids = cbind(rep(1, times = nrow(library_multistage_sample)),
                        library_multistage_sample$PSU_ID),
     probs = library_multistage_sample[,c("PSU_SAMPLING_PROB",
                                          "SSU_SAMPLING_PROB")]
 )

## End(Not run)