S.BE {TeachingSampling} | R Documentation |
Draws a Bernoulli sample withtout replacement of expected size $n$ from a population of size $N$
S.BE(N, prob)
N |
Population size |
prob |
Inclusion probability for each unit in the population |
The selected sample is drawn acording to a sequential procedure algorithm based on an uniform distribution. The Bernoulli sampling design is not a fixed sample size one.
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.
Hugo Andrés Gutiérrez Rojas hugogutierrez@usantotomas.edu.co
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.
Till'e, Y. (2006), Sampling Algorithms. Springer.
############ ## 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 Marco and Lucy data to draw a Bernoulli sample data(Marco) data(Lucy) attach(Lucy) 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,] data dim(data)