Estimating and Mapping Disaggregated Indicators
Standard and Extended Fay-Herriot Models for Disaggregated Indicators
Shows Plots for the Comparison of Estimates
Benchmark Function
Combines Sample and Population Data
Compare Function
Compare Predictions of Model Objects
Tranforms Dependent Variables
Direct Estimation of Disaggregated Indicators
Empirical Best Prediction for Disaggregated Indicators
A package for estimating and mapping disaggregated indicators
Summarizes an emdiObject
Fitted emdiObject
Presents Point, MSE and CV Estimates
Extract Fixed Effects from an emdi Object
Extract emdi Object Data
Extract Grouping Factors from an emdi Object
Extract Grouping Formula from an emdi Object
Extract Response Variable from an emdi Object
Extract Variance-covariance Matrix from an emdi Object
Confidence Intervals on Coefficients of an emdi Object
Loading the Shape File for Austrian Districts
Visualizes regional disaggregated estimates on a map
Plots for an emdi Object
Predictions from emdi Objects
Quantile-quantile Plots for an emdi Object
Extract Random Effects of emdi Objects
Spatial Autocorrelation Tests
Step Function
Exports an emdiObject to an Excel File or OpenDocument Spreadsheet
Functions that support estimating, assessing and mapping regional disaggregated indicators. So far, estimation methods comprise direct estimation, the model-based unit-level approach Empirical Best Prediction (see "Small area estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>), the area-level model (see "Estimates of income for small places: An application of James-Stein procedures to Census Data" by Fay and Herriot (1979) <doi:10.1080/01621459.1979.10482505>) and various extensions of it (adjusted variance estimation methods, log and arcsin transformation, spatial, robust and measurement error models), as well as their precision estimates. The assessment of the used model is supported by a summary and diagnostic plots. For a suitable presentation of estimates, map plots can be easily created. Furthermore, results can easily be exported to excel. For a detailed description of the package and the methods used see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07> and the second package vignette "A Framework for Producing Small Area Estimates Based on Area-Level Models in R".