The RankedSetSampling package provides a way for researchers to easily implement Ranked Set Sampling in practice.
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 8.887,10.198SBS 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 2.451,3.247Citing 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.