datasaeu.ns {sae.prop}

Data generated based on Univariate Fay Herriot Model with Additive Logistic Transformation with Non-Sampled Cases

Description

This data is generated based on univariate Fay-Herriot model and then transformed by using inverse Additive Logistic Transformation (alr). Then some domain would be edited to be non-sampled. The steps are as follows:

1. \beta are set to be \beta_{0} = \beta_{1} = \beta_{2} = 1

2. Auxiliary variables are set as follows:

   • x_{1} \sim N(0, 1)

   • x_{2} \sim N(0.5, 1)

3. For random effects, u \sim N(0, V_{u}), where V_{u} = 1.

4. For sampling errors e \sim N(0, V_{ed}), where V_{ed} is generated V_{ed} \sim InvGamma(50, 0.5).

5. The generated data is transformed using inverse alr transformation, so the data will be within the range of proportion.

6. Domain 3, 15, and 25 are set to be examples of non-sampled cases (0, 1, or NA).

7. cluster is cluster performed using k-medoids algorithm with pamk.

Auxiliary variables x_{1}, x_{2}, direct estimation y, and sampling variance vardir are combined into a data frame called datasaeu.

Usage

datasaeu.ns

Format

A data frame with 30 rows and 5 columns:

y

Direct Estimation of y

x1

Auxiliary variable of x1

x2

Auxiliary variable of x2

vardir

Sampling Variance of y

cluster

Cluster of y