datasaeu.ns | R Documentation |
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:
\beta
are set to be \beta_{0} = \beta_{1} = \beta_{2} = 1
Auxiliary variables are set as follows:
x_{1} \sim N(0, 1)
x_{2} \sim N(0.5, 1)
For random effects, u \sim N(0, V_{u})
, where V_{u} = 1
.
For sampling errors e \sim N(0, V_{ed})
, where V_{ed}
is generated V_{ed} \sim InvGamma(50, 0.5)
.
The generated data is transformed using inverse alr transformation, so the data will be within the range of proportion.
Domain 3, 15, and 25 are set to be examples of non-sampled cases (0, 1, or NA).
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.
datasaeu.ns
A data frame with 30 rows and 5 columns:
Direct Estimation of y
Auxiliary variable of x1
Auxiliary variable of x2
Sampling Variance of y
Cluster of y
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