TeachingSampling

An R package that draws complex samples and estimates complex parameters

TeachingSampling allows you to select samples from the most common probabilistic sampling designs and estimate complex parameters such as totals, means, ratios, regression coefficients, and quantiles.

The package is based on:

Gutierrez, H. A. (2009). Estrategias de muestreo: diseño de encuestas y estimación de parámetros. Editorial Universidad Santo Tomás.

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Installation

Stable version from CRAN

install.packages("TeachingSampling")

Development version from GitHub

install.packages("devtools")
devtools::install_github("psirusteam/TeachingSampling")

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Functions

Sampling designs

Function	Description
S.SI()	Simple random sampling without replacement
S.SY()	Systematic sampling
S.BE()	Bernoulli sampling
S.PO()	Poisson sampling
S.WR()	Simple random sampling with replacement
S.PPS()	PPS sampling with replacement
S.piPS()	PPS sampling without replacement
S.STSI()	Stratified simple random sampling
S.STPPS()	Stratified PPS sampling with replacement
S.STpiPS()	Stratified PPS sampling without replacement

Inclusion probabilities

Function	Description
PikPPS()	Inclusion probabilities proportional to size
PikSTPPS()	Inclusion probabilities for stratified PPS
PikHol()	Optimal inclusion probabilities (Holmberg)
Pik()	First-order inclusion probabilities from design
Pikl()	Second-order inclusion probabilities

Estimation

Function	Description
E.SI()	Estimation under simple random sampling
E.SY()	Estimation under systematic sampling
E.BE()	Estimation under Bernoulli sampling
E.PO()	Estimation under Poisson sampling
E.WR()	Estimation under with-replacement sampling
E.PPS()	Hansen-Hurwitz estimator under PPS-WR
E.piPS()	HT estimator under piPS sampling
E.STSI()	Estimation under stratified SI
E.STPPS()	Estimation under stratified PPS-WR
E.STpiPS()	Estimation under stratified piPS
E.1SI()	Estimation under single-stage cluster sampling
E.2SI()	Estimation under two-stage SI sampling
E.UC()	Estimation using the Ultimate Cluster method
E.Quantile()	Weighted quantile estimation
E.Trim()	Weight trimming and redistribution

Regression and calibration

Function	Description
E.Beta()	Regression coefficient estimation
GREG.SI()	Generalised regression estimator
Wk()	GREG calibration weights
IPFP()	Iterative proportional fitting (raking)

Variance estimation

Function	Description
VarHT()	Exact Horvitz-Thompson variance
VarSYGHT()	HT and Sen-Yates-Grundy variance estimators
HT()	Horvitz-Thompson estimator
Deltakl()	Matrix of joint inclusion probability differences

Sampling support (small populations)

Function	Description
Support()	Sampling support for SI designs
SupportWR()	Sampling support for WR designs
SupportRS()	Complete support for all sample sizes
Ik()	Sample membership indicator matrix
IkWR()	Frequency indicator matrix for WR sampling
IkRS()	Indicator matrix for all sample sizes
OrderWR()	Ordered WR sampling support
nk()	Frequency matrix for WR sampling
p.WR()	Sample probabilities under WR sampling

Allocation

Function	Description
kish_allocation()	Kish compromise allocation for stratified sampling

Utilities

Function	Description
Domains()	Domain indicator matrix
T.SIC()	Cluster totals for single-stage sampling

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Usage example

library(TeachingSampling)

data("Lucy")
N <- nrow(Lucy)
n <- 400

# Draw a simple random sample without replacement
sam <- S.SI(N, n)
sam <- sam[sam != 0]

# Estimate population totals
y <- data.frame(Income      = Lucy$Income[sam],
                Expenditure = Lucy$Expenditure[sam])

E.SI(N, n, y)

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Authors

Hugo Andrés Gutiérrez Rojas — Package author and maintainer
Email: hagutierrezro@gmail.com
GitHub: @psirusteam

Yury Vanessa Ochoa Montes Email: yury.ochoa@urosario.edu.co

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Support

• 📖 Reference manual (CRAN)
• CRAN page
• Report an issue

Comments, corrections, and suggestions are always welcome.