Learn R Programming

⚠️There's a newer version (1.3.1) of this package.Take me there.

surveysd

This is the development place for the R-package surveysd. The package can be used to estimate the standard deviation of estimates in complex surveys using bootstrap weights.

Installation

# Install release version from CRAN
install.packages("surveysd")

# Install development version from GitHub
devtools::install_github("statistikat/surveysd")

Concept

Bootstrapping has long been around and used widely to estimate confidence intervals and standard errors of point estimates. This package aims to combine all necessary steps for applying a calibrated bootstrapping procedure with custom estimating functions.

Workflow

A typical workflow with this package consists of three steps. To see these concepts in practice, please refer to the getting started vignette.

  • Calibrated weights can be generated with the function ipf() using an iterative proportional updating algorithm.
  • Bootstrap samples are drawn with rescaled bootstrapping in the function draw.bootstrap().
  • These samples can then be calibrated with an iterative proportional updating algorithm using recalib().
  • Finally, estimation functions can be applied over all bootstrap replicates with calc.stError().

Further reading

More information can be found on the github-pages site for surveysd.

Copy Link

Version

Install

install.packages('surveysd')

Monthly Downloads

325

Version

1.2.0

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Maintainer

Johannes Gussenbauer

Last Published

February 5th, 2020

Functions in surveysd (1.2.0)

calc.stError

Calcualte point estimates and their standard errors using bootstrap weights.
recalib

Calibrate weights
print.surveysd

Print function for surveysd objects
rescaled.bootstrap

Draw bootstrap replicates
plot.surveysd

Plot surveysd-Objects
kishFactor

Kish Factor
cpp_mean

Calculate mean by factors
generate.HHID

Generate new houshold ID for survey data with rotating panel design taking into account split households
PointEstimates

Weighted Point Estimates
ipf

Iterative Proportional Fitting
ipf_step

Perform one step of iterative proportional updating
computeLinear

Numerical weighting functions
demo.eusilc

Generate multiple years of EU-SILC data
draw.bootstrap

Draw bootstrap replicates