TeachingSampling (version 4.1.1)

S.BE: Bernoulli Sampling Without Replacement

Description

Draws a Bernoulli sample without replacement of expected size $n$ from a population of size $N$

Usage

S.BE(N, prob)

Arguments

N

Population size

prob

Inclusion probability for each unit in the population

Value

The function returns a vector of size \(N\). Each element of this vector indicates if the unit was selected. Then, if the value of this vector for unit \(k\) is zero, the unit \(k\) was not selected in the sample; otherwise, the unit was selected in the sample.

Details

The selected sample is drawn according to a sequential procedure algorithm based on an uniform distribution. The Bernoulli sampling design is not a fixed sample size one.

References

Sarndal, C-E. and Swensson, B. and Wretman, J. (1992), Model Assisted Survey Sampling. Springer. Gutierrez, H. A. (2009), Estrategias de muestreo: Diseno de encuestas y estimacion de parametros. Editorial Universidad Santo Tomas. Tille, Y. (2006), Sampling Algorithms. Springer.

See Also

E.BE

Examples

# NOT RUN {
############
## Example 1
############
# Vector U contains the label of a population of size N=5
U <- c("Yves", "Ken", "Erik", "Sharon", "Leslie")
# Draws a Bernoulli sample without replacement of expected size n=3
# The inlusion probability is 0.6 for each unit in the population
sam <- S.BE(5,0.6)
sam
# The selected sample is
U[sam]

############
## Example 2
############
# Uses the Lucy data to draw a Bernoulli sample

data(Lucy)
attach(Lucy)
N <- dim(Lucy)[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,0.01669)
# The information about the units in the sample is stored in an object called data
data <- Lucy[sam,]
data
dim(data)
# }