sps

An R package for sequential Poisson sampling

https://github.com/marberts/sps

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An R package for sequential Poisson sampling

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "100%"
)
```

# Sequential Poisson sampling 


[![CRAN status](https://www.r-pkg.org/badges/version/sps)](https://cran.r-project.org/package=sps)
[![sps status badge](https://marberts.r-universe.dev/badges/sps)](https://marberts.r-universe.dev)
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[![DOI](https://zenodo.org/badge/326323827.svg)](https://zenodo.org/doi/10.5281/zenodo.10109857)
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Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units, and is commonly used for price-index surveys. This package gives functions to draw stratified sequential Poisson samples according to the method by Ohlsson (1998), as well as other order sample designs by Rosén (1997), and generate approximate bootstrap replicate weights according to the generalized bootstrap method by Beaumont and Patak (2012).

## Installation

Get the stable release from CRAN.

```{r, eval=FALSE}
install.packages("sps")
```

The development version can be installed from R-Universe

```{r, eval=FALSE}
install.packages(
  "sps",
  repos = c("https://marberts.r-universe.dev", "https://cloud.r-project.org")
)
```

or directly from GitHub.

```{r, eval=FALSE}
pak::pak("marberts/sps")
```

## Usage

Given a vector of sizes for units in a population (e.g., revenue for sampling businesses) and a desired sample size, a stratified sequential Poisson sample can be drawn with the `sps()` function. 

```{r}
library(sps)

# Generate some data on sizes for 12 businesses in a single
# stratum as a simple example
revenue <- c(1:10, 100, 150)

# Draw a sample of 6 businesses
(samp <- sps(revenue, 6))

# Design weights and sampling strata are stored with the sample
weights(samp)
levels(samp)
```

Allocations are often proportional to size when drawing such samples, and the `prop_allocation()` function provides a variety of methods for generating proportional-to-size allocations.

```{r}
# Add some strata
stratum <- rep(c("a", "b"), c(9, 3))

# Make an allocation
(allocation <- prop_allocation(revenue, 6, stratum))

# Draw a stratified sample
(samp <- sps(revenue, allocation, stratum))

weights(samp)
levels(samp)
```

The design weights for a sample can then be used to generate bootstrap replicate weights with the `sps_repweights()` function.

```{r}
sps_repweights(weights(samp), 5)
```

The vignette gives more detail about how to use these functions to draw coordinated samples, top up a sample, and estimate variance.

## Prior work

There are many packages on CRAN for drawing samples proportional to size, but these generally do not include the sequential Poisson method. The **sampling** package contains a function for drawing sequential Poisson samples, but it does not allow for stratification, take-all units, or the use of permanent random numbers. By contrast, the **prnsamplr** package allows for the use of stratification and permanent random numbers with Pareto order sampling, but does not feature other order-sampling methods (like sequential Poisson).

## Contributing

All contributions are welcome. Please start by opening an issue on GitHub to report any bugs or suggest improvements and new features. See the contribution guidelines for this project for more information.

## References

Beaumont, J.-F. and Patak, Z. (2012). On the Generalized Bootstrap for Sample Surveys with Special Attention to Poisson Sampling. *International Statistical Review*, 80(1): 127-148.

Ohlsson, E. (1998). Sequential Poisson Sampling. *Journal of Official Statistics*, 14(2): 149-162.

Rosén, B. (1997). On sampling with probability proportional to size. *Journal of Statistical Planning and Inference*, 62(2): 159-191.

Owner

Citation (CITATION.cff)

# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "sps" in publications use:'
type: software
license: MIT
title: 'sps: Sequential Poisson Sampling'
version: 0.6.2
doi: 10.5281/zenodo.10109857
identifiers:
- type: doi
  value: 10.32614/CRAN.package.sps
abstract: Sequential Poisson sampling is a variation of Poisson sampling for drawing
  probability-proportional-to-size samples with a given number of units, and is commonly
  used for price-index surveys. This package gives functions to draw stratified sequential
  Poisson samples according to the method by Ohlsson (1998, ISSN:0282-423X), as well
  as other order sample designs by Rosén (1997, <https://doi.org/10.1016/S0378-3758(96)00186-3>),
  and generate appropriate bootstrap replicate weights according to the generalized
  bootstrap method by Beaumont and Patak (2012, <https://doi.org/10.1111/j.1751-5823.2011.00166.x>).
authors:
- family-names: Martin
  given-names: Steve
  email: marberts@protonmail.com
  orcid: https://orcid.org/0000-0003-2544-9480
preferred-citation:
  type: manual
  title: 'sps: Sequential Poisson Sampling'
  authors:
  - family-names: Martin
    given-names: Steve
    orcid: https://orcid.org/0000-0003-2544-9480
    email: marberts@protonmail.com
  year: '2025'
  doi: 10.5281/zenodo.10109857
  url: https://cran.r-project.org/package=sps
  notes: R package version 0.6.2
repository: https://CRAN.R-project.org/package=sps
repository-code: https://github.com/marberts/sps
url: https://marberts.github.io/sps/
contact:
- family-names: Martin
  given-names: Steve
  email: marberts@protonmail.com
  orcid: https://orcid.org/0000-0003-2544-9480
keywords:
- cran
- official-statistics
- r
- r-package
- rstats
- sampling
- statistics
- survey-sampling
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    given-names: Hadley
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