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README.Rmd
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README.Rmd
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---
output: github_document
bibliography: inst/Reference.bib
nocite: |
@Ozturk2021
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures",
out.width = "100%"
)
```
# RankedSetSampling
```{r, echo = FALSE}
description <- read.dcf("DESCRIPTION")
version <- as.vector(description[, "Version"])
min.r <- substr(description[, "Depends"], 7, 11)
```
<!-- badges: start -->
[](http://www.repostatus.org/#wip)
[](https://codecov.io/gh/biometryhub/RankedSetSampling?branch=main)
[](https://github.com/biometryhub/RankedSetSampling/actions)
<br>
[](https://cran.r-project.org/)
[`-orange.svg?style=flat-square)](/commits/main)
[)`-yellowgreen.svg)](/commits/main)
[](http://choosealicense.com/licenses/mit/)
<!-- badges: end -->
The RankedSetSampling package provides a way for researchers to easily implement Ranked Set Sampling in practice.
## Table of Contents
<!-- vim-markdown-toc GFM -->
* [Sampling Methods](#sampling-methods)
* [JPS Sampling](#jps-sampling)
* [RSS Sampling](#rss-sampling)
* [Installation](#installation)
* [Examples](#examples)
* [JPS Sample and Estimator](#jps-sample-and-estimator)
* [SBS PPS Sample and Estimator](#sbs-pps-sample-and-estimator)
* [Citing this package](#citing-this-package)
* [Related Reference](#related-reference)
<!-- vim-markdown-toc -->
## Sampling Methods
### JPS Sampling
Sampling is made following the diagram below.
### RSS Sampling
Sampling is made following the diagram below.
## Installation
Use the following code to install this package:
```{r eval=F}
if (!require("remotes")) install.packages("remotes")
remotes::install_github("biometryhub/RankedSetSampling", upgrade = FALSE)
```
## Examples
### JPS Sample and Estimator
<details>
<summary>JPS sample and estimator</summary>
``` r
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
```
</details>
### SBS PPS Sample and Estimator
<details>
<summary>SBS PPS sample and estimator</summary>
``` r
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
```
</details>
# Citing this package
This package can be cited using `citation("RankedSetSampling")` which generates
```{r echo=F, comment = NA}
citation("RankedSetSampling")
```
# Related Reference