NonProbEst

Estimation in Nonprobability Sampling

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–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

0

Last month

0

Last week

0

Average per day

0

Daily downloads

Total downloads

Description file content

Package
NonProbEst
Type
Package
Title
Estimation in Nonprobability Sampling
Version
0.2.2
Author
Luis Castro Martín , Ramón Ferri García and María del Mar Rueda
Maintainer
Luis Castro Martín
Description
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–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.
License
GPL (>= 2)
Encoding
UTF-8
LazyData
true
Imports
caret, sampling, e1071
RoxygenNote
7.0.1
NeedsCompilation
no
Packaged
2019-11-29 11:03:27 UTC; luis
Repository
CRAN
Date/Publication
2019-11-29 11:40:02 UTC

install.packages('NonProbEst')

0.2.2

16 days ago

Luis Castro Martín

GPL (>= 2)

Imports

caret, sampling, e1071

Discussions