Estimate individual level risk using individual case data and spatially aggregated control data
Released Oct 29, 2012 by Michelle Stanton
This package uses weighted estimating equations to estimate regression parameters in the intensity function of an inhomogeneous spatial Poisson process, when information on risk-factors is available at the individual level for cases, but only at a spatially aggregated level for the population at risk. Data-driven methods are used to select the weights used in the estimating equations. A formal test is available to detect non-Poisson behaviour in the underlying point process.
This package can be included as a dependency from a Java or Scala project by including
the following your project's
about embedding Renjin in JVM-based projects.
<dependencies> <dependency> <groupId>org.renjin.cran</groupId> <artifactId>aggrisk</artifactId> <version>1.0-b253</version> </dependency> </dependencies> <repositories> <repository> <id>bedatadriven</id> <name>bedatadriven public repo</name> <url>https://nexus.bedatadriven.com/content/groups/public/</url> </repository> </repositories>
If you're using Renjin from the command line, you load this library by invoking:
This package was last tested against Renjin 0.8.2543 on Dec 17, 2017.