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stratifiedsampling

Different methods for stratified populations.

https://github.com/rjauslin/stratifiedsampling

Science Score: 36.0%

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Last synced: 10 months ago · JSON representation

Repository

Different methods for stratified populations.

Basic Info
  • Host: GitHub
  • Owner: RJauslin
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 123 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 0
Created almost 6 years ago · Last pushed over 1 year ago
Metadata Files
Readme Changelog

README.Rmd 

---
output: github_document
---



```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```
# StratifiedSampling package

In this R package, different functions are implemented for selecting samples . 

* If the population of interest is stratified. Different functions are implemented, for more details see .
* If two datasets are available for statistical matching. A method based on optimal transport is implemented, for more details see .
* If you are interested in the Sequential Spatially Balanced method. 

The package contains also some useful functions. Look at the manual of the package for more information. 

## Installation

### CRAN version

```
install.packages("StratifiedSampling")
```

### Latest version 

You can install the latest version of the package `StratifiedSampling` with the following command:

``` r
# install.packages("devtools")
devtools::install_github("Rjauslin/StratifiedSampling")
```

## Optimal transport matching

The package proposes a method to do statistical matching using optimal transport and balanced sampling. For more details see Raphaël Jauslin and Yves Tillé (2021) . A complete example on how to use the package to make an optimal statistical transport match can be found in the following vignette:

```
vignette("ot_matching", package = "StratifiedSampling")
```

## Sequential spatially balanced sampling

The package proposes a method to select a well-spread sample balanced on some auxiliary variables. For more details see Raphaël Jauslin and Yves Tillé (2022) . A complete example on how to use the different functions to select a well-spread and balanced sample can be found in the following vignette:
```
vignette("sequential_balanced", package = "StratifiedSampling")
```


## Simple example on stratified population

Integrating a stratified structure in the population in a sampling design can considerably reduce the variance of the Horvitz-Thompson estimator. We propose in this package different methods to handle the selection of a balanced sample in stratified population. For more details see Raphaël Jauslin, Esther Eustache and Yves Tillé (2021) .



This basic example shows you how to set up a stratified sampling design. The example is done on the `swissmunicipalities` dataset from the package `sampling`.

```{r}
library(sampling)
library(StratifiedSampling)

data(swissmunicipalities)
swiss <- swissmunicipalities
X <- cbind(swiss$HApoly,
        swiss$Surfacesbois,
        swiss$P00BMTOT,
        swiss$P00BWTOT,
        swiss$POPTOT,
        swiss$Pop020,
        swiss$Pop2040,
        swiss$Pop4065,
        swiss$Pop65P,
        swiss$H00PTOT )

X <- X[order(swiss$REG),]
strata <- swiss$REG[order(swiss$REG)]
```

Strata are NUTS region of the Switzerland. Inclusion probabilities `pik` is set up equal within strata and such that the sum of the inclusion probabilities within strata is equal to 80.

```{r}
pik <- sampling::inclusionprobastrata(strata,rep(80,7))
```


It remains to use the function `stratifiedcube()`.

```{r}
s <- stratifiedcube(X,strata,pik)
```



We can check that we have correctly selected the sample. It is balanced and have the right number of units selected in each stratum.

```{r}
head(s)

sum(s)
t(X/pik)%*%s
t(X/pik)%*%pik

Xcat <- disj(strata)

t(Xcat)%*%s
t(Xcat)%*%pik

```

Owner

  • Name: Raphaël Jauslin
  • Login: RJauslin
  • Kind: user
  • Location: Neuchâtel
  • Company: Université de Neuchâtel

GitHub Events

Total
  • Push event: 3
Last Year
  • Push event: 3

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Last synced: over 2 years ago

All Time
  • Total Commits: 93
  • Total Committers: 3
  • Avg Commits per committer: 31.0
  • Development Distribution Score (DDS): 0.194
Past Year
  • Commits: 11
  • Committers: 2
  • Avg Commits per committer: 5.5
  • Development Distribution Score (DDS): 0.091
Top Committers
Name Email Commits
JAUSLIN Raphaël r****n@u****h 75
Rjauslin j****l@g****m 17
Raphaël Jauslin 4****n 1
Committer Domains (Top 20 + Academic)
unine.ch: 1

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Last synced: 11 months ago

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Past Year
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Top Authors
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  • rsbivand (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 241 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 6
  • Total maintainers: 1
cran.r-project.org: StratifiedSampling

Different Methods for Stratified Sampling

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 241 Last month
Rankings
Forks count: 28.8%
Dependent packages count: 29.8%
Stargazers count: 35.2%
Dependent repos count: 35.5%
Average: 36.1%
Downloads: 51.5%
Maintainers (1)
raphael.jauslin@bfs.admin.ch
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • Matrix * depends
  • R >= 3.5.0 depends
  • MASS * imports
  • Rcpp * imports
  • Rglpk * imports
  • proxy * imports
  • sampling * imports
  • transport * imports
  • BalancedSampling * suggests
  • StatMatch * suggests
  • geojsonio * suggests
  • ggplot2 * suggests
  • knitr * suggests
  • laeken * suggests
  • prettydoc * suggests
  • rgeos * suggests
  • rmapshaper * suggests
  • rmarkdown * suggests
  • sf * suggests
  • stats * suggests
  • testthat * suggests
  • viridis * suggests
.github/workflows/rhub.yaml actions
  • r-hub/actions/checkout v1 composite
  • r-hub/actions/platform-info v1 composite
  • r-hub/actions/run-check v1 composite
  • r-hub/actions/setup v1 composite
  • r-hub/actions/setup-deps v1 composite
  • r-hub/actions/setup-r v1 composite