The R Package NonProbEst for Estimation in Non-probability Surveys

Authors

M. Rueda, University of Granada
R. Ferri-García, University of Granada
L. Castro, University of Granada

Date of this Version

6-2020

Document Type

Article

Citation

The R Journal (June 2020) 12(1); Editor: Michael J. Kane

Comments

Copyright 2020, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

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.