E.SI {TeachingSampling} | R Documentation |
Computes the Horvitz-Thompson estimator of the population total according to an SI sampling design
E.SI(N, n, y)
N |
Population size |
n |
Sample size |
y |
Vector, matrix or data frame containig the recollected information of the variables of interest for every 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 an SI sampling 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.
############ ## Example 1 ############ # Uses the Marco Lucy data to draw a random sample of units according to a SI design data(Marco) data(Lucy) N <- dim(Marco)[1] n <- 400 sam <- S.SI(N,n) # The information about the units in the sample is stored in an object called data data <- Lucy[sam,] attach(data) names(data) # 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.SI(N,n,estima) ############ ## Example 2 ############ # Following with Example 1. The variable SPAM is a domain of interest Doma <- Domains(SPAM) # This function allows to estimate the parameters of the variables of interest # for every category in the domain SPAM estima <- data.frame(Income, Employees, Taxes) SPAM.no <- estima*Doma[,1] SPAM.yes <- estima*Doma[,2] E.SI(N,n,SPAM.no) E.SI(N,n,SPAM.yes)