envisim_samplr
| Crates.io | envisim_samplr |
| lib.rs | envisim_samplr |
| version | 0.2.0 |
| source | src |
| created_at | 2024-09-19 11:42:33.286437 |
| updated_at | 2024-09-24 13:23:56.22776 |
| description | Sampling methods for balanced and spatially balanced sampling |
| homepage | https://envisim.se |
| repository | https://github.com/envisim/rust-samplr |
| max_upload_size | |
| id | 1380176 |
| size | 172,215 |
(wpmg)
documentation
README
envisim_samplr
Provides design-based sampling methods, with a focus on spatially balanced and balanced sampling designs.
"everything is related to everything else, but near things are more related than distant things"
— Tobler's first law of geography, Waldo Tobler
Balanced sampling utilizes auxilliary information in order to obtain a sample where the Horvitz-Thompson (HT) estimator of the total of the auxilliary information equals the population total of the auxilliaries. This may be very efficient (yield relatively low variance) if there is a linear relationship between the auxilliaries and the variable of interest.1
Spatially balanced sampling uses auxilliary information in order to obtain a sample that is well-spread in auxilliary space, as well as being balanced. The samples can then be seen as a miniature version of the population. This generally yields low variances for the variable of interest, if there is a general relationship between the auxilliaries and the variables of interest.2
Links
Footnotes
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Grafström, A., & Tillé, Y. (2013). Doubly balanced spatial sampling with spreading and restitution of auxiliary totals. Environmetrics, 24(2), 120-131. https://doi.org/10.1002/env.2194 ↩
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Grafström, A., & Schelin, L. (2014). How to select representative samples. Scandinavian Journal of Statistics, 41(2), 277-290. https://doi.org/10.1111/sjos.12016 ↩