| Name : R-NonProbEst
| |
| Version : 0.2.4
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp156.29.1
| Date : 2024-09-10 03:05:17
|
| Group : Development/Libraries/Other
| Source RPM : R-NonProbEst-0.2.4-lp156.29.1.src.rpm
|
| Size : 0.19 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Estimation in Nonprobability Sampling
|
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.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.6/x86_64 |
