Function reference • ReGenesees

Preamble
`ReGenesees-package`	ReGenesees: a Package for Design-Based and Model-Assisted Analysis of Complex Sample Surveys
Survey Design
`e.svydesign()` `summary(<analytic>)`	Specification of a Complex Survey Design
`weights()`	Retrieve Sampling Units Weights
`find.lon.strata()`	Find Strata with Lonely PSUs
`collapse.strata()`	Collapse Strata Technique for Eliminating Lonely PSUs
`des.addvars()`	Add Variables to Design Objects
`des.merge()`	Merge New Survey Data into Design Objects
`smooth.strat.jump()`	Smooth Weights to Cope with Stratum Jumpers
Calibration
`pop.template()`	Template Data Frame for Known Population Totals
`population.check()`	Compliance Test for Known Totals Data Frames
`pop.desc()`	Natural Language Description of Known Totals Templates
`fill.template()`	Fill the Known Totals Template for a Calibration Task
`pop.plot()`	Plot Calibration Control Totals vs Current Estimates
`bounds.hint()`	A Hint for Range Restricted Calibration
`e.calibrate()`	Calibration of Survey Weights
`check.cal()`	Calibration Convergence Check
`trimcal()`	Trim Calibration Weights while Preserving Calibration Constraints
`g.range()`	Range of g-Weights
`get.residuals()`	Calibration Residuals of Interest Variables
`get.linvar()`	Linearized Variable(s) of Complex Estimators by Domains
`ext.calibrated()`	Make ReGenesees Digest Externally Calibrated Weights
`contrasts.RG()` `contrasts.off()` `contrasts.reset()` `contr.off()`	Set, Reset or Switch Off Contrasts for Calibration Models
`%into%` `%into%`	Compress Nested Factors
Special Purpose Calibration
`prep.calBeta()` `pop.calBeta()`	Calibration on Multiple Regression Coefficients
`pop.fuse()`	Fuse Control Totals Data Frames for Special Purpose and Ordinary Calibration Tasks
Estimates and Sampling Errors
`svystatTM()` `coef(<svystatTM>)` `SE(<svystatTM>)` `VAR(<svystatTM>)` `cv(<svystatTM>)` `deff(<svystatTM>)` `confint(<svystatTM>)`	Estimation of Totals and Means in Subpopulations
`svystatR()` `coef(<svystatR>)` `SE(<svystatR>)` `VAR(<svystatR>)` `cv(<svystatR>)` `deff(<svystatR>)` `confint(<svystatR>)`	Estimation of Ratios in Subpopulations
`svystatS()` `coef(<svystatS>)` `SE(<svystatS>)` `VAR(<svystatS>)` `cv(<svystatS>)` `deff(<svystatS>)` `confint(<svystatS>)`	Estimation of Shares in Subpopulations
`svystatSR()` `coef(<svystatSR>)` `SE(<svystatSR>)` `VAR(<svystatSR>)` `cv(<svystatSR>)` `deff(<svystatSR>)` `confint(<svystatSR>)`	Estimation of Share Ratios in Subpopulations
`svystatB()` `coef(<svystatB>)` `SE(<svystatB>)` `VAR(<svystatB>)` `cv(<svystatB>)` `deff(<svystatB>)` `confint(<svystatB>)` `summary(<svystatB>)`	Estimation of Population Regression Coefficients in Subpopulations
`svystatQ()` `coef(<svystatQ>)` `SE(<svystatQ>)` `VAR(<svystatQ>)` `cv(<svystatQ>)` `confint(<svystatQ>)`	Estimation of Quantiles in Subpopulations
`svystatL()` `coef(<svystatL>)` `SE(<svystatL>)` `VAR(<svystatL>)` `cv(<svystatL>)` `deff(<svystatL>)` `confint(<svystatL>)`	Estimation of Complex Estimators in Subpopulations
`svySigma()` `coef(<svySigma>)` `SE(<svySigma>)` `VAR(<svySigma>)` `cv(<svySigma>)` `confint(<svySigma>)`	Estimation of the Population Standard Deviation of a Variable
`svySigma2()` `coef(<svySigma2>)` `SE(<svySigma2>)` `VAR(<svySigma2>)` `cv(<svySigma2>)` `confint(<svySigma2>)`	Estimation of the Population Variance of a Variable
`svyDelta()` `details()` `coef(<svyDelta>)` `SE(<svyDelta>)` `VAR(<svyDelta>)` `cv(<svyDelta>)` `confint(<svyDelta>)`	Estimation of a Measure of Change from Two Not Necessarily Independent Samples
`aux.estimates()`	Quick Estimates of Auxiliary Variables Totals
`CoV()` `Corr()`	Design Covariance and Correlation of Complex Estimators in Subpopulations
`write.svystat()`	Export Survey Statistics
`SE()` `VAR()` `cv()` `deff()`	Extractor Functions for Variability Statistics
`ReGenesees.options`	Variance Estimation Options for the ReGenesees Package
Generalized Variance Functions Method
`GVF.db` `GVF.db$insert`() `GVF.db$delete`() `GVF.db$get`() `GVF.db$assign`() `GVF.db$reset`()	Archive of Registered GVF Models
`gvf.input()` `plot(<gvf.input>)`	Prepare Input Data to Fit GVF Models
`svystat()` `plot(<gvf.input.gr>)` `coef(<svystat.gr>)` `SE(<svystat.gr>)` `VAR(<svystat.gr>)` `cv(<svystat.gr>)` `deff(<svystat.gr>)` `confint(<svystat.gr>)`	Compute Many Estimates and Errors in Just a Single Shot
`fit.gvf()` `print(<gvf.fit>)` `print(<gvf.fits>)` `print(<gvf.fit.gr>)` `print(<gvf.fits.gr>)` `[`(<gvf.fits>) `[[`(<gvf.fits>) `summary(<gvf.fit>)` `summary(<gvf.fits>)` `summary(<gvf.fit.gr>)` `summary(<gvf.fits.gr>)`	Fit GVF Models
`plot(<gvf.fit>)` `plot(<gvf.fits>)` `plot(<gvf.fit.gr>)` `plot(<gvf.fits.gr>)`	Diagnostic Plots for Fitted GVF Models
`drop.gvf.points()`	Drop Outliers and Refit a GVF Model
`getR2()` `AIC(<gvf.fits>)` `BIC(<gvf.fits>)`	Quality Measures on Fitted GVF Models
`getBest()`	Identify the Best Fit GVF Model
`predictCV()`	Predict CV Values via Fitted GVF Models
`coef(<gvf.fit>)` `coef(<gvf.fits>)` `coef(<gvf.fit.gr>)` `coef(<gvf.fits.gr>)` `residuals(<gvf.fit>)` `residuals(<gvf.fits>)` `residuals(<gvf.fit.gr>)` `residuals(<gvf.fits.gr>)` `fitted(<gvf.fit>)` `fitted(<gvf.fits>)` `fitted(<gvf.fit.gr>)` `fitted(<gvf.fits.gr>)` `predict(<gvf.fit>)` `predict(<gvf.fits>)` `predict(<gvf.fit.gr>)` `predict(<gvf.fits.gr>)` `effects(<gvf.fit>)` `effects(<gvf.fits>)` `effects(<gvf.fit.gr>)` `effects(<gvf.fits.gr>)` `rstandard(<gvf.fit>)` `rstandard(<gvf.fits>)` `rstandard(<gvf.fit.gr>)` `rstandard(<gvf.fits.gr>)` `rstudent(<gvf.fit>)` `rstudent(<gvf.fits>)` `rstudent(<gvf.fit.gr>)` `rstudent(<gvf.fits.gr>)` `anova(<gvf.fit>)` `anova(<gvf.fits>)` `anova(<gvf.fit.gr>)` `anova(<gvf.fits.gr>)` `vcov(<gvf.fit>)` `vcov(<gvf.fits>)` `vcov(<gvf.fit.gr>)` `vcov(<gvf.fits.gr>)`	Miscellanea: Methods for Fitted GVF Models
`estimator.kind()`	Which Estimator Did Generate these Survey Statistics?
Sample Size and Power
`n.prop()` `prec.prop()` `n.comp2prop()` `pow.comp2prop()` `mde.comp2prop()`	Sample Size Requirements and Power Calculations for Proportions
`n.mean()` `prec.mean()` `n.comp2mean()` `pow.comp2mean()` `mde.comp2mean()`	Sample Size Requirements and Power Calculations for Means
Diagnostics and Utilities
`UWE()`	Unequal Weighting Effect
`Zapsmall(<default>)` `Zapsmall(<data.frame>)`	Zapsmall Data Frame Columns and Numeric Vectors
Data Sets
`data.examples`	Artificial Household Survey Data
`fpcdat`	A Small But Not Trivial Artificial Sample Data Set
`sbs`	Artificial Structural Business Statistics Data
`Delta.el`	Two Artificial Samples of Elementary Units for Estimation of Change
`Delta.clus`	Two Artificial Cluster Samples for Estimation of Change
`AF.gvf`	Example Data for GVF Model Fitting

Reference

Preamble

Survey Design

Calibration

Special Purpose Calibration

Estimates and Sampling Errors

Generalized Variance Functions Method

Sample Size and Power

Diagnostics and Utilities

Data Sets

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