GREG.SI {TeachingSampling} | R Documentation |
Computes the generalized regression estimator of the population total for several variables of interest under simple random sampling without replacement
GREG.SI(N, n, y, x, tx, b, b0=FALSE)
N |
The population size |
n |
The sample size |
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 |
tx |
Vector containing the populations totals of the auxiliary information |
b |
Vector of estimated regression coefficients |
b0 |
By default FALSE. The intercept of the regression model |
The function returns a vector of total population estimates for each variable 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: Linear models involving continuous auxiliary information ###################################################################### # Draws a simple random sample without replacement 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) # Vector of inclusion probabilities for units in the selected sample Pik<-rep(n/N,n) ########### common mean model ################### estima<-data.frame(Income, Employees, Taxes) x <- rep(1,n) tx <- c(N) b <- E.Beta(estima,x,Pik,ck=1,b0=FALSE) GREG.SI(N,n,estima,x,tx, b, b0=FALSE) ########### common ratio model ################### estima<-data.frame(Income) x <- data.frame(Employees) tx <- c(151950) b <- E.Beta(estima,x,Pik,ck=x,b0=FALSE) GREG.SI(N,n,estima,x,tx, b, b0=FALSE) ########### Simple regression model without intercept ################### estima<-data.frame(Income, Employees) x <- data.frame(Taxes) tx <- c(28654) b <- E.Beta(estima,x,Pik,ck=1,b0=FALSE) GREG.SI(N,n,estima,x,tx, b, b0=FALSE) ########### Multiple regression model without intercept ################### estima<-data.frame(Income) x <- data.frame(Employees, Taxes) tx <- c(151950, 28654) b <- E.Beta(estima,x,Pik,ck=1,b0=FALSE) GREG.SI(N,n,estima,x,tx, b, b0=FALSE) ########### Simple regression model with intercept ################### estima<-data.frame(Income, Employees) x <- data.frame(Taxes) tx <- c(N,28654) b <- E.Beta(estima,x,Pik,ck=1,b0=TRUE) GREG.SI(N,n,estima,x,tx, b, b0=TRUE) ########### Multiple regression model with intercept ################### estima<-data.frame(Income) x <- data.frame(Employees, Taxes) tx <- c(N, 151950, 28654) b <- E.Beta(estima,x,Pik,ck=1,b0=TRUE) GREG.SI(N,n,estima,x,tx, b, b0=TRUE) #################################################################### ## Example 2: Linear models involving discrete auxiliary information #################################################################### # Draws a simple random sample without replacement 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) # Vector of inclusion probabilities for units in the selected sample Pik<-rep(n/N,n) # The auxiliary information is discrete type Doma<-Domains(Level) ########### Poststratified common mean model ################### estima<-data.frame(Income, Employees, Taxes) tx <- c(83,737,1576) b <- E.Beta(estima,Doma,Pik,ck=1,b0=FALSE) GREG.SI(N,n,estima,Doma,tx, b, b0=FALSE) ########### Poststratified common ratio model ################### estima<-data.frame(Income, Employees) x<-Doma*Taxes tx <- c(6251,16293,6110) b <- E.Beta(estima,x,Pik,ck=1,b0=FALSE) GREG.SI(N,n,estima,x,tx, b, b0=FALSE)