Description Usage Arguments Details Value Author(s) References See Also Examples
Computes the Horvitz-Thompson estimator of the population total according to a πPS sampling design
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y |
Vector, matrix or data frame containing the recollected information of the variables of interest for every unit in the selected sample |
Pik |
Vector of inclusion probabilities for each unit in the selected sample |
Returns the estimation of the population total of every single variable of interest, its estimated variance and its estimated coefficient of variation under a πPPS sampling design. This function uses the results of approximate expressions for the estimated variance of the Horvitz-Thompson estimator
The function returns a data matrix whose columns correspond to the estimated parameters of the variables of interest
Hugo Andres Gutierrez Rojas hugogutierrez@usantotomas.edu.co
Matei, A. and Tille, Y. (2005), Evaluation of Variance Approximations and Estimators in Maximun
Entropy Sampling with Unequal Probability and Fixed Sample Design. Journal of Official Statistics. Vol 21, 4, 543-570.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | # Uses the Lucy data to draw a sample according to a piPS
# without replacement design
data(Lucy)
attach(Lucy)
# The inclusion probability of each unit is proportional to the variable Income
# The selected sample of size n=400
n <- 400
res <- S.piPS(n, Income)
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)
# Pik.s is the inclusion probability of every single unit in the selected sample
Pik.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.piPS(estima,Pik.s)
# Same results than HT function
HT(estima, Pik.s)
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