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README.Rmd
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---
output: github_document
---
<!-- 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/README-"
)
```
# bbw: Blocked Weighted Bootstrap <img src="man/figures/logo.png" width="200" align="right" />
<!-- badges: start -->
[](https://www.repostatus.org/#active)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://cran.r-project.org/package=bbw)
[](https://CRAN.R-project.org/package=bbw)
[](https://CRAN.R-project.org/package=bbw)
[](https://CRAN.R-project.org/package=bbw)
[](https://github.com/rapidsurveys/bbw/actions/workflows/R-CMD-check.yaml)
[](https://github.com/rapidsurveys/bbw/actions/workflows/test-coverage.yaml)
[](https://app.codecov.io/gh/rapidsurveys/bbw)
[](https://www.codefactor.io/repository/github/rapidsurveys/bbw)
[](https://doi.org/10.5281/zenodo.6594797)
<!-- badges: end -->
## Overview
The **blocked weighted bootstrap** is an estimation technique for use with data from two-stage cluster sampled surveys in which either prior weighting (e.g. *population-proportional sampling* or *PPS* as used in [Standardized Monitoring and Assessment of Relief and Transitions (SMART)](https://smartmethodology.org/) surveys) or *posterior weighting* (e.g. as used in [Rapid Assessment Method (RAM)](https://rapidsurveys.io/ramOPmanual/) and [Simple Spatial Sampling Method (S3M)](https://researchonline.lshtm.ac.uk/id/eprint/2572543) surveys) is implemented.
## Installation
You can install `{bbw}` from CRAN with:
```{r install_cran, eval = FALSE}
install.packages("bbw")
```
You can install the current development version of `{bbw}` from the [RapidSurveys R Universe](https://rapidsurveys.r-universe.dev) with:
```{r install_universe, eval = FALSE}
install.packages("bbw", repos = "https://rapidsurveys.r-universe.dev")
```
or through GitHub via the `{pak}` package with:
```{r install_github, eval = FALSE}
if(!require(pak)) install.packages("pak")
pak::pak("rapidsurveys/bbw")
```
## Usage
`{bbw}` as used in RAM and S3M is a modification to the *percentile bootstrap* to include *blocking* and *weighting* to account for a *complex sample design*.
With RAM and S3M surveys, the sample is complex in the sense that it is an unweighted cluster sample. Data analysis procedures need to account for the sample design. A blocked weighted bootstrap can be used:
<br/>
* **Blocked**: The block corresponds to the primary sampling unit (*PSU = cluster*). PSUs are resampled with replacement. Observations within the resampled PSUs are also sampled with replacement.
* **Weighted**: RAM and S3M samples do not use population proportional sampling (PPS) to weight the sample prior to data collection (e.g. as is done with SMART surveys). This means that a posterior weighting procedure is required. `{bbw}` uses a *"roulette wheel"* algorithm (see [illustration below](#fig1)) to weight (i.e. by population) the selection probability of PSUs in bootstrap replicates.
<a name="fig1"></a>
<p align="center">
```{r, echo = FALSE, eval = TRUE, out.width = "50%", fig.alt = "Roulette wheel algorithm", fig.align = "center"}
knitr::include_graphics("man/figures/rouletteWheel.png")
```
</p>
In the case of prior weighting by PPS all clusters are given the same weight. With posterior weighting (as in RAM or S3M) the weight is the population of each PSU. This procedure is very similar to the [*fitness proportional selection*](https://en.wikipedia.org/wiki/Fitness_proportionate_selection) technique used in *evolutionary computing*.
A total of `m` PSUs are sampled with replacement for each bootstrap replicate (where `m` is the number of PSUs in the survey sample).
The required statistic is applied to each replicate. The reported estimate consists of the 0.025th (*95% LCL*), 0.5th (*point estimate*), and 0.975th (*95% UCL*) quantiles of the distribution of the statistic across all survey replicates.
The main reason to use `{bbw}` is that the bootstrap allows a wider range statistics to be calculated than model-based techniques without resort to grand assumptions about the sampling distribution of the required statistic. A good example for this is the confidence interval on the difference between two medians which might be used for many socio-economic variables. The `{bbw}` also allows for a wider range of hypothesis tests to be used with complex sample survey data.
## Citation
If you use the {bbw} package in your work, please cite using the suggested citation provided by a call to the `citation()` function as follows:
```{r cite}
citation("bbw")
```
## Community guidelines
Feedback, bug reports, and feature requests are welcome; file issues or seek support [here](https://github.com/rapidsurveys/bbw/issues). If you would like to contribute to the package, please see our [contributing guidelines](https://rapidsurveys.io/bbw/CONTRIBUTING.html).
This project is released with a [Contributor Code of Conduct](https://rapidsurveys.io/bbw/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.