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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.