sampling (version 2.9)

regest_strata: The regression estimator for a stratified design

Description

Computes the regression estimator of the population total, using the design-based approach, for a stratified sampling. The same regression model is used for all strata. The underling regression model is a model without intercept.

Usage

regest_strata(formula,weights,Tx_strata,strata,pikl,
sigma=rep(1,length(weights)),description=FALSE)

Arguments

formula

the regression model formula (y~x).

weights

vector of the weights; its length is equal to n, the sample size.

Tx_strata

population total of x, the auxiliary variable.

strata

vector of stratum identificator.

pikl

the joint inclusion probabilities for the sample.

sigma

vector of positive values accounting for heteroscedasticity.

description

if TRUE, the following components are printed for each stratum: the Horvitz-Thompson estimator, the beta coefficients, their standard error, t_values, p_values, and the covariance matrix. By default, FALSE.

Value

The function returns the value of the regression estimator computed as the sum of the stratum estimators.

See Also

regest

Examples

# NOT RUN {
# generates artificial data
y=rgamma(10,3)
x=y+rnorm(10)
Stratum=c(1,1,2,2,2,3,3,3,3,3)
# population size
N=200
# sample size
n=10
# assume proportional allocation, nh/Nh=n/N 
pikl=matrix(0,n,n)
for(i in 1:n)
 {for(j in 1:n)
  if(i!=j)
      pikl[i,j]=pikl[j,i]=n*(n-1)/(N*(N-1))
  pikl[i,i]=n/N
  }
regest_strata(formula=y~x-1,weights=rep(N/n,n),Tx_strata=c(50,30,40),
strata=Stratum,pikl,description=TRUE)
# }