TeachingSampling (version 4.1.1)

E.PPS: 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

Value

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

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

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

S.PPS, HH

Examples

# NOT RUN {
# 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)
# }