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Combining a Probability and a Non-Probability Sample in a Capture-Recapture Setting

Combining a Probability and a Non-Probability Sample in a Capture-Recapture Setting - Published in JOSS (2018)

https://github.com/williamsbenjamin/blendr

Science Score: 95.0%

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    Found 4 DOI reference(s) in README and JOSS metadata
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Blending a Probablilty Sample and a Non-Probability Sample in a Capture-Recapture Setting

Basic Info
  • Host: GitHub
  • Owner: williamsbenjamin
  • License: gpl-3.0
  • Language: R
  • Default Branch: master
  • Size: 338 KB
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Created over 7 years ago · Last pushed almost 5 years ago
Metadata Files
Readme License

README.Rmd 

---
output: github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# blendR
[![DOI](http://joss.theoj.org/papers/10.21105/joss.00886/status.svg)](https://doi.org/10.21105/joss.00886)

The goal of blendR is to provide statistically valid estimators of total (and standard errors) when blending a non-probability sample with a probability sample. The two samples are considered to follow capture-recapture methodology, with the capture sample being the non-probability sample and the recapture sample being the probability sample. This package is based upon research by Liu et al (2017), Breidt, Opsomer, and Huang (2018), and dissertation research by the package author. Additional estimators will be released in future versions.

These estimators are important and currently used by Texas Parks and Wildlife to estimate the total number of Red Snapper fish caught. Combining data sources is an important research area due to the prevalence of big data in both industry and academia. These estimators can easily extend to other areas of interest, for example, the internet of things, insurance claims, and estimation of the death toll due to a natural disaster.

## Installation

You can install blendR from github with:

```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("williamsbenjamin/blendR")
```

## Example

An example using data from a capture-recapture sampling program in 2016 by Texas Parks and Wildlife
Captains could voluntarily self-report (via a smartphone app) about their catch of Red Snapper fish (non-probability sample) and could be sampled in a dockside intercept sample (probability sample). The self-reports are the capture sample and the dockside intercept is the recapture, probability sample.

```{r example}

library(tibble)
library(blendR)

## Dataset for boats sampled in the dockside intercept, if their captains also self-reported, that data included as well

red_snapper_sampled

## Dataset for the self-reported boats

self_reports 

s_design <- survey::svydesign(id = ~psu,
                              strat = ~stratum,
                              prob = ~prob,
                              nest = T,
                              data = red_snapper_sampled)

t_p(data = red_snapper_sampled,
    recapture_total = number_caught_ps,
    captured = captured_indicator,
    survey_design = s_design,
    capture_units = nrow(self_reports))
```

Owner

  • Name: Benjamin Williams
  • Login: williamsbenjamin
  • Kind: user
  • Location: Denver

Assistant Professor Business Information & Analytics University of Denver

JOSS Publication

Combining a Probability and a Non-Probability Sample in a Capture-Recapture Setting
Published
August 14, 2018
DOI
Volume 3, Issue 28, Page 886
Authors
Benjamin Williams ORCID
Department of Statistical Science, Southern Methodist University
Editor
Thomas J. Leeper ORCID
Tags
non-probability sampling combining data sources capture-recapture sampling
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Dependencies

DESCRIPTION cran
  • stats * imports
  • knitr * suggests
  • rmarkdown * suggests
  • testthat * suggests
  • tibble * suggests