E.Beta {TeachingSampling} | R Documentation |
Computes the estimation of regression coefficients using the principles of the Horvitz-Thompson estimator
E.Beta(y, x, Pik, ck=1, b0=FALSE)
y |
Vector, matrix or data frame containig the recollected information of the variables of interest for every unit in the selected sample |
x |
Vector, matrix or data frame containig the recollected auxiliary information for every unit in the selected sample |
Pik |
A vector containing the inclusion probabilities for each unit in the selected sample |
ck |
By default equals to one. It is a vector of weights induced by the structure of variance of the supposed model |
b0 |
By default FALSE. The intercept of the regression model |
Returns the estimation of the population regression coefficients in a supposed linear model
The function returns a vector whose entries correspond to the estimated parameters of the regression coefficients
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: Linear models involving continuous auxiliary information ###################################################################### # Draws a simple random sample without replacement data(Lucy) data(Marco) 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) # Vector of inclusion probabilities for the units in the sample Pik<-rep(n/N,n) ########### common mean model ################### estima<-data.frame(Income, Employees, Taxes) x <- rep(1,n) E.Beta(estima,x,Pik,ck=1,b0=FALSE) ########### common ratio model ################### estima<-data.frame(Income) x <- data.frame(Employees) E.Beta(estima,x,Pik,ck=x,b0=FALSE) ########### Simple regression model without intercept ################### estima<-data.frame(Income, Employees) x <- data.frame(Taxes) E.Beta(estima,x,Pik,ck=1,b0=FALSE) ########### Multiple regression model without intercept ################### estima<-data.frame(Income) x <- data.frame(Employees, Taxes) E.Beta(estima,x,Pik,ck=1,b0=FALSE) ########### Simple regression model with intercept ################### estima<-data.frame(Income, Employees) x <- data.frame(Taxes) E.Beta(estima,x,Pik,ck=1,b0=TRUE) ########### Multiple regression model with intercept ################### estima<-data.frame(Income) x <- data.frame(Employees, Taxes) E.Beta(estima,x,Pik,ck=1,b0=TRUE) #################################################################### ## Example 2: Linear models involving discrete auxiliary information #################################################################### # Draws a simple random sample without replacement data(Lucy) data(Marco) N <- dim(Marco)[1] n <- 400 sam <- S.SI(N,n) # The information about the sample units is stored in an object called data data <- Lucy[sam,] attach(data) names(data) # The auxiliary information Doma<-Domains(Level) # Vector of inclusion probabilities for the units in the sample Pik<-rep(n/N,n) ########### Poststratified common mean model ################### estima<-data.frame(Income, Employees, Taxes) E.Beta(estima,Doma,Pik,ck=1,b0=FALSE) ########### Poststratified common ratio model ################### estima<-data.frame(Income, Employees) x<-Doma*Taxes E.Beta(estima,x,Pik,ck=1,b0=FALSE)