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Changelog

Source: NEWS.md

nonprobsvy 0.2.0

Breaking changes

  • functions pop.size, controlSel, controlOut and controlInf were renamed to pop_size, control_sel, control_out and control_inf respectively.
  • function genSimData removed completely as it is not used anywhere in the package.
  • argument maxLik_method renamed to maxlik_method in the control_sel function.
  • control_out function:
    • predictive_match renamed to pmm_match_type to align with the PMM (Predictive Mean Matching) estimator naming convention, where all related parameters start with pmm_
  • control_sel function:
    • argument method removed as it was not used
    • argument est_method_sel renamed to est_method
    • argument h renamed to gee_h_fun to make this more readable to the user
    • start_type now accepts only zero and mle (for gee models only).
  • control_inf function:
    • bias_inf renamed to vars_combine and type changed to logical. TRUE if variables (its levels) should be combined after variable selection algorithm for the doubly robust approach.
    • pi_ij – argument removed as it is not used.
  • nonprobsvy class renamed to nonprob and all related method adjusted to this change
  • functions logit_model_nonprobsvy, probit_model_nonprobsvy and cloglog_model_nonprobsvy removed in the favour of more readable method_ps function that specifies the propensity score model
  • new option control_inference=control_inf(vars_combine=TRUE) which allows doubly robust estimator to combine variables prior estimation i.e. if selection=~x1+x2 and y~x1+x3 then the following models are fitted selection=~x1+x2+x3 and y~x1+x2+x3. By default we set control_inference=control_inf(vars_combine=FALSE). Note that this behaviour is assumed independently from variable selection.

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 check_balance 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.
  • citation file added.
  • new generic methods added:
    • weights – returns IPW weights
    • update – allows to update the nonprob class object
  • new functions added and exported:
    • method_ps – for modelling propensity score
    • method_glm – for modelling y using glm function
    • method_nn – for the NN method
    • method_pmm – for the PMM method
    • method_npar – for the non-parametric method
  • new print.nonprob, summary.nonprob and print.nonprob_summary methods, i.e.
> result_mi
A nonprob object
 - estimator type: mass imputation
 - method: glm (gaussian)
 - auxiliary variables source: survey
 - vars selection: false
 - variance estimator: analytic
 - population size fixed: false
 - naive (uncorrected) estimators:
   - variable y1: 3.1817
   - variable y2: 1.8087
 - selected estimators:
   - variable y1: 2.9498 (se=0.0420, ci=(2.8674, 3.0322))
   - variable y2: 1.5760 (se=0.0326, ci=(1.5122, 1.6399))

number of digits can be changed using print(x, digits) as shown below

> print(result_mi,2)
A nonprob object
 - estimator type: mass imputation
 - method: glm (gaussian)
 - auxiliary variables source: survey
 - vars selection: false
 - variance estimator: analytic
 - population size fixed: false
 - naive (uncorrected) estimators:
   - variable y1: 3.18
   - variable y2: 1.81
 - selected estimators:
   - variable y1: 2.95 (se=0.04, ci=(2.87, 3.03))
   - variable y2: 1.58 (se=0.03, ci=(1.51, 1.64))
> summary(result_mi) |> print(digits=2)
A nonprob_summary object
 - call: nonprob(data = subset(population, flag_bd1 == 1), outcome = y1 + 
    y2 ~ x1 + x2, svydesign = sample_prob)
 - estimator type: mass imputation
 - nonprob sample size: 693011 (69.3%)
 - prob sample size: 1000 (0.1%)
 - population size: 1000000 (fixed: false)
 - detailed information about models are stored in list element(s): "outcome"
----------------------------------------------------------------
 - distribution of outcome residuals:
   - y1: min: -4.79; mean: 0.00; median: 0.00; max: 4.54
   - y2: min: -4.96; mean: -0.00; median: -0.07; max: 12.25
 - distribution of outcome predictions (nonprob sample):
   - y1: min: -2.72; mean: 3.18; median: 3.04; max: 16.28
   - y2: min: -1.55; mean: 1.81; median: 1.58; max: 13.92
 - distribution of outcome predictions (prob sample):
   - y1: min: -0.46; mean: 2.95; median: 2.84; max: 10.31
   - y2: min: -0.58; mean: 1.58; median: 1.39; max: 7.87
----------------------------------------------------------------

Bugfixes

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

Other

  • more informative error messages added.
  • documentation improved.
  • switching completely to snake_case.
  • extensive cleaning of the code.
  • more unit-tests added.
  • new dependencies: formula.tools

Documentation

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

Replication materials

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 \hat{y}-\hat{y} in predictive mean matching imputation and predictive_match = 2 corresponds to \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.