nonprobsvy 0.1.1

Bugfixes

• bug Fix occuring 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

• add information to summary about quality of estimation basing on difference between estimated and known total values of auxiliary variables
• add estimation of exact standard error for k-nearest neighbor estimator.
• add breaking change to controlOut function by switching values for predictive_match argument. From now on, the predictive_match = 1 means \(\hat{y}-\hat{y}\) in predictive mean matching imputation and predictive_match = 2 corresponds to \(\hat{y}-y\) matching.
• implement div option when variable selection (more in documentation) for doubly robust estimation.
• add 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.
• add estimated inclusion probabilities and its derivatives for probability and non-probability samples to nonprob output when IPW or DR model executed.
• add 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

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.