Ecological inference

Small area estimation

Model comparison in the presence of non-ignorable missing responses


Software for ecological inference

We provide software for the R and WinBUGS statistical packages, to implement our framework for combining ecological with individual-level data. These are intended to be useful to practitioners wishing to use these models in an applied context.

All the software on this page is freely available, and can be redistributed under the terms of the GNU General Public Licence.

Overview

The software implements models for inferring the individual-level relationships between exposures and outcomes, using aggregate data alone, individual data alone, or a combination of aggregate and individual data. The models are described in our article Improving ecological inference using individual-level data (Statistics in Medicine, in press. PDF working copy). To summarise, suppose we have one or both of These contain

R package ecoreg

We have developed a package ecoreg for the R statistical software. ecoreg fits a range of models for this form of ecological and individual data, using maximum likelihood estimation. Models with random area-level intercepts are supported, with likelihoods calculated using Gauss-Hermite integration.

This is available from the CRAN repository of R packages. A detailed guide to the methodology and use of ecoreg is available with the package, in the file ecoreg-guide.pdf in the doc subdirectory of the installed package, and in standard R help pages, for example help(eco). If you are new to R, please read its documentation, beginning with the manual An Introduction to R, to familiarise yourself with the environment.

Queries or bug reports on this software should be addressed to Chris Jackson.

A short tutorial (or "vignette") with examples on how to use the package is included in the package, and can also be downloaded here (PDF).

Windows installation
Download the binary package ecoreg_0.1.1.zip, and unzip into your R library tree. The library tree is commonly c:\Program Files\R\rw2011\library, where rw2011 changes according to the current version of R.
Linux / Unix / Mac OS X installation
Download the source package ecoreg_0.1.1.tar.gz, unzip into any directory, and execute from the command line
R CMD INSTALL ecoreg.
A detailed guide to the methodology and use of ecoreg is available with the package, in the file ecoreg-guide.pdf in the doc subdirectory of the installed package, and in standard R help pages, for example help(eco). If you are new to R, please read its documentation, beginning with the manual An Introduction to R, to familiarise yourself with the environment.

This is a pre-release, development version of ecoreg. When officially released, ecoreg will be published at the Comprehensive R Archive Network (CRAN), the official repository for contributed R packages.

WinBUGS resources

Here we provide compound documents for WinBUGS 1.4, containing model specifications, example data, instructions and documentation for a range of models for ecological and individual data. One document is provided for each specific model. From these examples, users familiar with WinBUGS should be able to adapt the format of the model specification and data for their own specific case. See the BUGS website for more information about WinBUGS.

Files to accompany Improving ecological inference using individual-level data (Statistics in Medicine, 2006)

eco2-agg.odc
Ecological inference with one binary and one continuous exposure, and aggregate data alone.
eco2-indiv.odc
Ecological inference with one binary and one continuous exposure, and individual data alone.
eco2-agg-indiv.odc
Ecological inference with one binary and one continuous exposure, and combined individual and aggregate data.
eco2-agg-indiv-spatial.odc
Ecological inference with one binary and one continuous exposure, combined individual and aggregate data, and spatially-correlated random effects.
eco3-agg-indiv.odc
Ecological inference with three binary exposures, where the within-area joint distribution of the exposures is available.

Files to accompany Hierarchical related regression for combining aggregate and individual data in studies of socio-economic disease risk factors

hrr.odc
Hierarchical related regression for hospital admission for cardiovascular disease.

Note: By clicking the above links, the files may not be displayed properly. But the original .odc files can be downloaded here and can then be opened in WinBUGS.

Queries or bug reports on this software should be addressed to Chris Jackson.

Files to accompany workshop on Introduction to methods for analysis of combined individual and aggregate social science data

(course details including course notes can be found here.)

Winbugs code for practical demonstration


Software for small-area estimation

R package SAE

Small area estimation using EBLUP estimators. (Virgilio Gómez Rubio, Nicola Salvati).

A short tutorial (or "vignette") comparing various small area estimation methods, is included in the package, and can also be downloaded here (PDF).

Windows installation
Download the binary package SAE_0.07.zip, and unzip into your R library tree. The library tree is commonly c:\Program Files\R\R-2.4.1\library, where R-2.4.1 changes according to the current version of R.
Linux / Unix / Mac OS X installation
Download the source package SAE_0.07.tar.gz, unzip into any directory, and execute from the command line
R CMD INSTALL SAE.

Queries or bug reports on SAE should be addressed to Virgilio Gómez Rubio.

WinBUGS code for SAE

Models for full data

The following code implementes some Spatial Bayesian Models for Small Area Estimatio when the target variable is Normal. See the working paper on Bayesian Small Area Estimation for details.

Area Level Model
Area Level Model with unstructured and spatially correlated random effects.
Unit Level Model 1
Unit Level Model with unstructured and spatially correlated random effects, and same within area variation.
Unit Level Model 2
Unit Level Model with unstructured and spatially correlated random effects, and different within area variation.
Unit Level Model 3
Unit Level Model with unstructured and spatially correlated random effects, and hierarchical structure on the different within area variation.
Models with missing data

The following code is similar but it can handle missing data, i.e., provide estimates in areas that have not been included in the survey.

Area Level Model (missing data)
Area Level Model with unstructured and spatially correlated random effects.
Unit Level Model 1 (missing data)
Unit Level Model with unstructured and spatially correlated random effects, and same within area variation.
Unit Level Model 2 (missing data)
Unit Level Model with unstructured and spatially correlated random effects, and different within area variation.
Unit Level Model 3 (missing data)
Unit Level Model with unstructured and spatially correlated random effects, and hierarchical structure on the different within area variation.
Models for the ranking of areas

The following code implements Small Area Estimation and different methods for the ranking of areas. The value 29 and 58 correspond to the rank of the 10% and 20% areas with the lowest income used in our examples and should be changed.

Area Level Model
Area Level Model with unstructured and spatially correlated random effects.
Area Level Model (missing data)
Area Level Model with unstructured and spatially correlated random effects.
Unit Level Model 2
Unit Level Model with unstructured and spatially correlated random effects, and different within area variation.
Unit Level Model 2 (missing data)
Unit Level Model with unstructured and spatially correlated random effects, and different within area variation.

Software for comparing models in the presence of non-ignorable missing responses

An R script containing the functions used for calculating the DIC based on the observed data likelihood (DICO) presented in the paper, Using DIC to compare selection models with non-ignorable missing responses.

An illustrative example of the DICO functions

An example of the use of the DICO functions can be implemented by downloading the files below and executing the commands in the example R script, which calls the functions in DICOcodeWebV1.R.

DICO.zip contains all 4 files.