RankedSetSampling

The RankedSetSampling package provides a way for researchers to easily implement Ranked Set Sampling in practice.

Table of Contents

• Sampling Methods
  • JPS Sampling
  • RSS Sampling
• Installation
• Examples
  • JPS Sample and Estimator
  • SBS PPS Sample and Estimator
• Citing this package
• Related Reference

Sampling Methods

JPS Sampling

Sampling is made following the diagram below.

JPS sampling diagram

RSS Sampling

Sampling is made following the diagram below.

RSS sampling diagram

Installation

Use the following code to install this package:

if (!require("remotes")) install.packages("remotes")
remotes::install_github("biometryhub/RankedSetSampling", upgrade = FALSE)

Examples

JPS Sample and Estimator

JPS sample and estimator

  set.seed(112)
  population_size <- 600
  # the number of samples to be ranked in each set
  H <- 3

  with_replacement <- FALSE
  sigma <- 4
  mu <- 10
  n_rankers <- 3
  # sample size
  n <- 30

  rhos <- rep(0.75, n_rankers)
  taus <- sigma * sqrt(1 / rhos^2 - 1)
  population <- qnorm((1:population_size) / (population_size + 1), mu, sigma)

  data <- RankedSetSampling::jps_sample(population, n, H, taus, n_rankers, with_replacement)
  data <- data[order(data[, 2]), ]

  RankedSetSampling::rss_jps_estimate(
    data,
    set_size = H,
    method = "JPS",
    confidence = 0.80,
    replace = with_replacement,
    model_based = FALSE,
    pop_size = population_size
  )
  #>          Estimator Estimate Standard Error 80% Confidence intervals
  #> 1       UnWeighted    9.570          0.526               8.88,10.26
  #> 2      Sd.Weighted    9.595          0.569             8.849,10.341
  #> 3 Aggregate Weight    9.542          0.500             8.887,10.198
  #> 4     JPS Estimate    9.502          0.650             8.651,10.354
  #> 5     SRS estimate    9.793          0.783             8.766,10.821
  #> 6          Minimum    9.542          0.500             8.887,10.198

SBS PPS Sample and Estimator

SBS PPS sample and estimator

  set.seed(112)

  # SBS sample size, PPS sample size
  sample_sizes <- c(5, 5)

  n_population <- 233
  k <- 0:(n_population - 1)
  x1 <- sample(1:13, n_population, replace = TRUE) / 13
  x2 <- sample(1:8, n_population, replace = TRUE) / 8
  y <- (x1 + x2) * runif(n = n_population, min = 1, max = 2) + 1
  measured_sizes <- y * runif(n = n_population, min = 0, max = 4)

  population <- matrix(cbind(k, x1, x2, measured_sizes), ncol = 4)
  sample_result <- sbs_pps_sample(population, sample_sizes)

  # estimate the population mean and construct a confidence interval
  df_sample <- sample_result$sample
  sample_id <- df_sample[, 1]
  y_sample <- y[sample_id]

  sbs_pps_estimates <- sbs_pps_estimate(
    population, sample_sizes, y_sample, df_sample,
    n_bootstrap = 100, alpha = 0.05
  )
  print(sbs_pps_estimates)
  #>   n1 n2 Estimate  St.error 95% Confidence intervals
  #> 1  5  5    2.849 0.1760682              2.451,3.247

Citing this package

This package can be cited using citation("RankedSetSampling") which generates

To cite package 'RankedSetSampling' in publications use:

  Ozturk O, Rogers S, Kravchuk O, Kasprzak P (2021).
  _RankedSetSampling: Easing the Application of Ranked Set Sampling in
  Practice_. R package version 0.1.0,
  <https://biometryhub.github.io/RankedSetSampling/>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {RankedSetSampling: Easing the Application of Ranked Set Sampling in Practice},
    author = {Omer Ozturk and Sam Rogers and Olena Kravchuk and Peter Kasprzak},
    year = {2021},
    note = {R package version 0.1.0},
    url = {https://biometryhub.github.io/RankedSetSampling/},
  }

Related Reference

Ozturk, Omer, and Olena Kravchuk. 2021. “Judgment Post-Stratified Assessment Combining Ranking Information from Multiple Sources, with a Field Phenotyping Example.” Journal of Agricultural, Biological and Environmental Statistics. https://doi.org/10.1007/s13253-021-00439-1.