E.PPS {TeachingSampling} | R Documentation |
Estimation of the Population Total under Probability Proportional to Size Sampling With Replacement
Description
Computes the Hansen-Hurwitz estimator of the population total according to a probability proportional to size sampling with replacement design
Usage
E.PPS(y, pk)
Arguments
y |
Vector, matrix or data frame containing the recollected information of the variables of interest for every unit in the selected sample |
pk |
A vector containing selection probabilities for each unit in the sample |
Details
Returns the estimation of the population total of every single variable of interest, its estimated standard error and its estimated coefficient of variation estimated under a probability proportional to size sampling with replacement design
Value
The function returns a data matrix whose columns correspond to the estimated parameters of the variables of interest
Author(s)
Hugo Andres Gutierrez Rojas hagutierrezro@gmail.com
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.
See Also
Examples
# Uses the Lucy data to draw a random sample according to a
# PPS with replacement design
data(Lucy)
attach(Lucy)
# The selection probability of each unit is proportional to the variable Income
m <- 400
res <- S.PPS(m,Income)
# The selected sample
sam <- res[,1]
# The information about the units in the sample is stored in an object called data
data <- Lucy[sam,]
attach(data)
names(data)
# pk.s is the selection probability of each unit in the selected sample
pk.s <- res[,2]
# 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.PPS(estima,pk.s)