E.BE {TeachingSampling} | R Documentation |
Computes the Horvitz-Thompson estimator of the population total according to a BE sampling design
E.BE(y, prob)
y |
Vector, matrix or data frame containig the recollected information of the variables of interest for every unit in the selected sample |
prob |
Inclusion probability for each unit in the population |
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.
# Uses the Marco and Lucy data to draw a Bernoulli sample data(Lucy) data(Marco) N <- dim(Marco)[1] # The population size is 2396. If the expected sample size is 400, # then, the inclusion probability must be 400/2396=0.1669 sam <- S.BE(N,0.1669) # 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.BE(estima,0.1669)