Population size estimation from multiple incomplete lists, with particular attention to sparse data and model-selection uncertainty.
The MultipleSystemsEstimation R package implements Poisson log-linear methods for multiple systems estimation of population size. Working from a table of counts of cases falling on a number of lists and their possible overlaps, the aim is to estimate the size of the dark figure — individuals appearing on none of the lists.
Among other developments, the package addresses two important challenges: sparse capture data, including combinations of lists with no observed overlap, and the proper assessment of the uncertainty arising from the selection of a model from a large range of possibilities.
Key features
- A range of alternative estimation methods: BIC-based model selection, stepwise selection, Bayesian thresholding and fixed-model estimation.
- Model-selection uncertainty: Bootstrap confidence intervals that account for model selection, rather than conditioning on the selected model.
- Sparse data: Methods for dealing properly with zero capture counts and for testing the existence and identifiability of estimates.
- Hierarchical models: Tools for generating, exploring and comparing hierarchical log-linear models. The implementation goes beyond the previously published papers in extending all methods to handle higher-order interactions.
- Consistent interface: A common structure for estimates, uncertainty intervals, fitted models and diagnostic information.
Documentation
The User Guide is the recommended starting point. It explains the estimation methods, their application and the interpretation of results.
Five companion vignettes provide further details of the methods and their implementation.
References
- Chan, L., Silverman, B. W. and Vincent, K. (2021). Multiple systems estimation for sparse capture data: inferential challenges When there are nonoverlapping lists. Journal of the American Statistical Association, 116(535), 1297–1306. https://doi.org/10.1080/01621459.2019.1708748
- Silverman, B. W. (2020). Multiple-systems analysis for the quantification of modern slavery: classical and Bayesian approaches. Journal of the Royal Statistical Society: Series A (Statistics in Society), 183(3), 691–736. doi:10.1111/rssa.12505..
- Silverman, B. W., Chan, L. and Vincent, K. (2024). Bootstrapping multiple systems estimates to account for model selection. Statistics and Computing, 34, article 44. https://doi.org/10.1007/s11222-023-10346-9