total_estimation

A data frame containing the sample with the variables for which the estimated population totals are to be calculated.

sample

A vector of pre-calculated weights.

weights

String vector specifying the variables in the sample to be estimated.

estimated_vars

An integer specifying the population size.

Estimates the population totals for the specified variables measured in a sample given some pre-calculated weights.

Different inference procedures are proposed in the literature to correct for selection bias that might be introduced with non-random selection mechanisms. A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA).
The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitz<e2><80><93>Thompson type estimators. This method is usually used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding information of auxiliary variables.
Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities.
The package 'NonProbEst' implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample.

total_estimation: Estimates the population totals

Description

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Examples