S.PO {TeachingSampling} | R Documentation |
Draws a Poisson sample of expected size $n$ from a population of size $N$
S.PO(N, Pik)
N |
Population size |
Pik |
Vector of inclusion probabilities for each unit in the population |
The selected sample is drawn acording to a sequential procedure algorithm based on a uniform distribution. The Poisson 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 # "Erik" is drawn in every possible sample becuse its inclusion probability is one Pik <- c(0.5, 0.2, 1, 0.9, 0.5) sam <- S.PO(5,Pik) sam # The selected sample is U[sam] ############ ## Example 2 ############ # Uses the Marco and Lucy data to draw a Poisson sample data(Marco) data(Lucy) attach(Lucy) N <- dim(Lucy)[1] # The population size is 2396. The expected sample size is 400, # The inclusion probability is proportional to the variable Income n<-400 Pik<-n*Income/sum(Income) # None element of Pik bigger than one which(Pik>1) # The selected sample sam <- S.PO(N,Pik) # The information about the units in the sample is stored in an object called data data <- Lucy[sam,] data dim(data)