dataTwofold {saeHB.twofold} | R Documentation |
A dataset to simulate Small Area Estimation using Hierarchical Bayesian method under Two Fold Subarea level model with Normal distribution on variabel interest.
This data is generated by these following steps:
Generate sampling error e_{ij}
,subarea random effect u_{ij}
, area random effect v_{i}
, auxiliary variabel x_{ij1},x_{ij2}
, and weight or proportions of unit w_{ij}
Generate subarea random effect u_{ij}
~N(0,8)
Generate area random effect v_{i}
~ N(0,8)
Generate auxilary variabel on subarea level x_{ij1}
~ U(0,1)
Generate auxilary variabel on subarea level x_{ij2}
~N(10,1)
Generate unit proportion on each subarea w_{ij}
~U(10,20)
Generate sampling error e_{ij}
~N(0,\sigma^{2}_{e})
where \sigma^{2}_{e}
~IG(1,1)
is a variance of direct estimator
Setting coefficient \beta_{0}=\beta_{1}=\beta_{2} =1
Calculate target parameter \mu_{ij}=\beta_{0} +\beta_{1}x_{ij1} +\beta_{2}x_{ij2}+v_{i}+u_{ij}
Calculate direct estimator y_{ij}=\mu_{ij}+e_{ij}
Auxiliary variables x_{ij1}
,x_{ij2}
, direct estimation (y_{ij}
) ,vardir, and weight w_{ij}
are combined in a dataframe called dataTwofold
dataTwofold
A data frame with 90 rows and 6 columns:
Direct estimation of subarea mean y_{ij}
Auxiliary variabel of x_{ij1}
Auxiliary variabel of x_{ij2}
Index that describes the code relating to warea for each subarea
Unit proportion on each subarea or weight w_{ij}
Sampling variance of direct estimator y_{ij}