| 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
Särndal, C-E. and Swensson, B. and Wretman, J. (1992), Model Assisted Survey Sampling. Springer.
Gutiérrez, H. A. (2009), Estrategias de muestreo: Diseño de encuestas y estimación de parámetros.
Editorial Universidad Santo Tomás.
######################################################################
## 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)