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Changelog

Source: NEWS.md

nonprobsvy 0.1.2

Features

  • two additional datasets have been included: jvs (Job Vacancy Survey; a probability sample survey) and admin (Central Job Offers Database; a non-probability sample survey). The units and auxiliary variables have been aligned in a way that allows the data to be integrated using the methods implemented in this package.
  • a nonprobsvycheck function was added to check the balance in the totals of the variables based on the weighted weights between the non-probability and probability samples.
  • Important - the functions controlSel, controlOut and controlInf have been replaced by their counterparts control_sel, control_out and control_inf.

Bugfixes

  • basic methods and functions related to variance estimation, weights and probability linking methods have been rewritten in a more optimal and readable way.

Documentation

  • annotation has been added that arguments such as strata, subset and na_action are not supported for the time being.

nonprobsvy 0.1.1

CRAN release: 2024-11-14

Bugfixes

  • bug Fix occurring when estimation was based on auxiliary variable, which led to compression of the data from the frame to the vector.
  • bug Fix related to not passing maxit argument from controlSel function to internally used nleqslv function
  • bug Fix related to storing vector in model_frame when predicting y_hat in mass imputation glm model when X is based in one auxiliary variable only - fix provided converting it to data.frame object.

Features

  • added information to summary about quality of estimation basing on difference between estimated and known total values of auxiliary variables
  • added estimation of exact standard error for k-nearest neighbor estimator.
  • added breaking change to controlOut function by switching values for predictive_match argument. From now on, the predictive_match = 1 means ŷ−ŷ\hat{y}-\hat{y} in predictive mean matching imputation and predictive_match = 2 corresponds to ŷ−y\hat{y}-y matching.
  • implemented div option when variable selection (more in documentation) for doubly robust estimation.
  • added more insights to nonprob output such as gradient, hessian and jacobian derived from IPW estimation for mle and gee methods when IPW or DR model executed.
  • added estimated inclusion probabilities and its derivatives for probability and non-probability samples to nonprob output when IPW or DR model executed.
  • added model_frame matrix data from probability sample used for mass imputation to nonprob when MI or DR model executed.

Unit tests

  • added unit tests for variable selection models and mi estimation with vector of population totals available

nonprobsvy 0.1.0

CRAN release: 2024-04-04

Features

  • implemented population mean estimation using doubly robust, inverse probability weighting and mass imputation methods
  • implemented inverse probability weighting models with Maximum Likelihood Estimation and Generalized Estimating Equations methods with logit, complementary log-log and probit link functions.
  • implemented generalized linear models, nearest neighbours and predictive mean matching methods for Mass Imputation
  • implemented bias correction estimators for doubly-robust approach
  • implemented estimation methods when vector of population means/totals is available
  • implemented variables selection with SCAD, LASSO and MCP penalization equations
  • implemented analytic and bootstrap (with parallel computation - doSNOW package) variance for described estimators
  • added control parameters for models
  • added S3 methods for object of nonprob class such as
    • nobs for samples size
    • pop.size for population size estimation
    • residuals for residuals of the inverse probability weighting model
    • cooks.distance for identifying influential observations that have a significant impact on the parameter estimates
    • hatvalues for measuring the leverage of individual observations
    • logLik for computing the log-likelihood of the model,
    • AIC (Akaike Information Criterion) for evaluating the model based on the trade-off between goodness of fit and complexity, helping in model selection
    • BIC (Bayesian Information Criterion) for a similar purpose as AIC but with a stronger penalty for model complexity
    • confint for calculating confidence intervals around parameter estimates
    • vcov for obtaining the variance-covariance matrix of the parameter estimates
    • deviance for assessing the goodness of fit of the model

Unit tests

  • added unit tests for IPW estimators.

Github repository

  • added automated R-cmd check

Documentation

  • added documentation for nonprob function.