Different inference procedures are proposed in the literature to correct selection bias that might be introduced with non-random sampling mechanisms. The R package NonProbEst enables the estimation of parameters using some of these techniques to correct selection bias in non-probability surveys. The mean and the total of the target variable are estimated using Propensity Score Adjustment, calibration, statistical matching, model-based, model-assisted and model-calibratated techniques. Confidence intervals can also obtained for each method. Machine learning algorithms can be used for estimating the propensities or for predicting the unknown values of the target variable for the non-sampled units. Variance of a given estimator is performed by two different Leave-One-Out jackknife procedures. The functionality of the package is illustrated with example data sets.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2020-015.zip
NonProbEst, caret, sampling, survey
OfficialStatistics, HighPerformanceComputing, MachineLearning, Multivariate, SocialSciences, Survival
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For attribution, please cite this work as
Rueda, et al., "The R package NonProbEst for estimation in non-probability surveys", The R Journal, 2020
BibTeX citation
@article{RJ-2020-015,
author = {Rueda, M. and Ferri-García, R. and Castro, L.},
title = {The R package NonProbEst for estimation in non-probability surveys},
journal = {The R Journal},
year = {2020},
note = {https://doi.org/10.32614/RJ-2020-015},
doi = {10.32614/RJ-2020-015},
volume = {12},
issue = {1},
issn = {2073-4859},
pages = {406-418}
}