E.BE {TeachingSampling}R Documentation

Estimation of the Population Total under Bernoulli Sampling Without Replacement

Description

Computes the Horvitz-Thompson estimator of the population total according to a BE sampling design

Usage

E.BE(y, prob)

Arguments

y Vector, matrix or data frame containig the recollected information of the variables of interest for every unit in the selected sample
prob Inclusion probability for each unit in the population

Details

Returns the estimation of the population total of every single variable of interest, its estimated variance and its estimated coefficient of variation under an SI sampling design

Value

The function returns a data matrix whose columns correspond to the estimated parameters of the variables of interest

Author(s)

Hugo Andrés Gutiérrez Rojas hugogutierrez@usantotomas.edu.co

References

Sarndal, C-E. and Swensson, B. and Wretman, J. (1992), Model Assisted Survey Sampling. Springer.
Guti'errez, H. A. (2009), Estrategias de muestreo: Dise~no de encuestas y estimaci'on de par'ametros. Editorial Universidad Santo Tom'as.

See Also

S.BE

Examples

# Uses the Marco and Lucy data to draw a Bernoulli sample
data(Lucy)
data(Marco)

N <- dim(Marco)[1]
# The population size is 2396. If the expected sample size is 400,
# then, the inclusion probability must be 400/2396=0.1669
sam <- S.BE(N,0.1669)
# The information about the units in the sample is stored in an object called data
data <- Lucy[sam,]
attach(data)
names(data)
# The variables of interest are: Income, Employees and Taxes
# This information is stored in a data frame called estima
estima <- data.frame(Income, Employees, Taxes)
E.BE(estima,0.1669)

[Package TeachingSampling version 0.7.6 Index]