Domains {TeachingSampling} | R Documentation |
Creates a matrix of domain indicator variables for every single unit in the selected sample or in the entire population
Domains(y)
y |
Vector of the domain of interest containing the membership of each unit to a specified category of the domain |
Each value of y represents the doamin which a specified unit belongs
The function returns a ntimes p matrix, where n is the number of units in the selected sample and p is the number of categories of the domain of interest. The values of this matrix are zero, if the unit does not belogns to a specified category and one, otherwise.
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 ############ # This domain contains only two categories: "yes" and "no" x <- as.factor(c("yes","yes","yes","no","no","no","no","yes","yes")) Domains(x) ############ ## Example 2 ############ # Uses the Marco and 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 <- sample(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 variable SPAM is a domain of interest Doma <- Domains(SPAM) Doma # HT estimation of the absolute domain size for every category in the domain # of interest E.SI(N,n,Doma) ############ ## Example 3 ############ # Following with Example 2... # The variables of interest are: Income, Employees and Taxes # This function allows to estimate the population total of this variables for every # category in the domain of interest 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)