datamix: datamix

Description Usage Format

Description

This data generated by simulation based on Fay-Herriot with Measurement Error Model by following these steps:

  1. Generate x1i from a UNIF(5, 10) distribution, x2i from a UNIF(9, 11) distribution, ψi = 3, c1i = c2i = 0.25, and σ2v = 2.

  2. Generate u1i from a N(0, c1i) distribution, u2i from a N(0, c2i) distribution, ei from a N(0, ψi) distribution, and vi from a N(0, σ2v) distribution.

  3. Generate x3i from a UNIF(1, 5) distribution and x4i from a UNIF(10, 14) distribution.

  4. Generate x.hat1i = x1i + u1i and x.hat2i = x2i + u2i.

  5. Then for each iteration, we generated Yi = 2 + 0.5*x.hat1i + 0.5*x.hat2 i + 2*x3i + 0.5*x4i + vi and yi = Yi + ei.

This data contain combination between auxiliary variable measured with error and without error. Direct estimator y, auxiliary variable x.hat1 x.hat2 x3 x4, sampling variance ψ, and c1 c2 are arranged in a dataframe called datamix.

Usage

1

Format

A data frame with 100 observations on the following 8 variables.

small_area

areas of interest.

y

direct estimator for each domain.

x.hat1

auxiliary variable (measured with error) for each domain.

x.hat2

auxiliary variable (measured with error) for each domain.

x3

auxiliary variable (measured without error) for each domain.

x4

auxiliary variable (measured without error) for each domain.

vardir

sampling variances for each domain.

var.x1

mean squared error of auxiliary variable and sorted as x.hat1

var.x2

mean squared error of auxiliary variable and sorted as x.hat2


saeME documentation built on Jan. 13, 2021, 11:03 a.m.