E.PPS {TeachingSampling} | R Documentation |
Computes the Hansen-Hurwitz estimator of the population total according to a probability proportional to size sampling with replacement design
E.PPS(y, pk)
y |
Vector, matrix or data frame containig the recollected information of the variables of interest for every unit in the selected sample |
pk |
A vetor containing selection probabilities for each unit in the sample |
Returns the estimation of the population total of every single variable of interest, its estimated variance and its estimated coefficient of variation estimated under a probability proportional to size sampling with replacement design
The function returns a data matrix whose columns correspond to the estimated parameters of the variables of interest
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.
# Uses the Marco and Lucy data to draw a random sample according to a # PPS with replacement design data(Marco) data(Lucy) attach(Lucy) # The selection probability of each unit is proportional to the variable Income res <- S.PPS(400,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)