regest_strata: Regression estimator for a stratified design

In sampling: Survey Sampling

Regression estimator for a stratified design

Description

Computes the regression estimator of the population total for a stratified sampling, using the design-based approach. 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	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	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 estimated beta coefficients, their estimated 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

# 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 
# joint inclusion probabilities (for the sample)
pikl=matrix(n*(n-1)/(N*(N-1)),n,n)
diag(pikl)=n/N
regest_strata(formula=y~x-1,weights=rep(N/n,n),Tx_strata=c(50,30,40),
strata=Stratum,pikl,description=TRUE)

sampling documentation built on Nov. 2, 2023, 6:26 p.m.