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nonprobsvy News and Updates

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.
• argument nonprob(weights=NULL) replaced to nonprob(case_weights=NULL) to stress that this refer to case weights not sampling or other weights in non-probability sample

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.
• argument na_action with default na.omit
• 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

> 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 argument strata is not supported for the time being.

Replication materials

• to verify the quality of the software please refer to the replication materials available here: https://github.com/ncn-foreigners/software-tutorials

nonprobsvy 0.1.1

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

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.