Package: NonProbEst 0.2.4
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.
Authors:
NonProbEst_0.2.4.tar.gz
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NonProbEst_0.2.4.tgz(r-4.4-any)NonProbEst_0.2.4.tgz(r-4.3-any)
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NonProbEst.pdf |NonProbEst.html✨
NonProbEst/json (API)
# Install 'NonProbEst' in R: |
install.packages('NonProbEst', repos = c('https://luiscastro193.r-universe.dev', 'https://cloud.r-project.org')) |
- population - A full population
- sampleNP - A non-probabilistic sample
- sampleP - A probabilistic sample
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 years agofrom:978c6f6d64. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 21 2024 |
R-4.5-win | NOTE | Nov 21 2024 |
R-4.5-linux | NOTE | Nov 21 2024 |
R-4.4-win | NOTE | Nov 21 2024 |
R-4.4-mac | NOTE | Nov 21 2024 |
R-4.3-win | NOTE | Nov 21 2024 |
R-4.3-mac | NOTE | Nov 21 2024 |
Exports:calib_weightsconfidence_intervalfast_jackknife_variancegeneric_jackknife_variancejackknife_variancelee_weightsmatchingmean_estimationmodel_assistedmodel_basedmodel_calibratedprop_estimationpropensitiessc_weightstotal_estimationvalliant_weightsvd_weights
Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2glmnetglobalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlpSolvelubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppEigenrecipesreshape2rlangrpartsamplingscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Weights of the calibration estimator | calib_weights |
Confidence interval | confidence_interval |
Calculates Jackknife variance without reweighting | fast_jackknife_variance |
Calculates Jackknife variance with reweighting for an arbitrary estimator | generic_jackknife_variance |
Calculates Jackknife variance with reweighting for PSA | jackknife_variance |
Calculates Lee weights | lee_weights |
Predicts unknown responses by matching | matching |
Estimates the population means | mean_estimation |
Calculates a model assisted estimation | model_assisted |
Calculates a model based estimation | model_based |
Calculates a model calibrated estimation | model_calibrated |
A full population | population |
Estimates the population proportion | prop_estimation |
Calculates sample propensities | propensities |
A non-probabilistic sample | sampleNP |
A probabilistic sample | sampleP |
Calculates Schonlau and Couper weights | sc_weights |
Estimates the population totals | total_estimation |
Calculates Valliant weights | valliant_weights |
Calculates Valliant and Dever weights | vd_weights |