Goodness of Fit Noise Analysis Using Monte Carlo Techniques
Released Dec 8, 2016 by Joseph G Kreke
Goodness-of-fit metrics, such as R-Squared, RMSE, etc., share a sensitivity to noise, dependent on the degrees of freedom. Some metrics, such as R-Squared, decrease with increasing dof and some, such as RMSE, increase with increasing dof. This package calculates the noise baseline (ceiling) by random sampling, calculating the metric’s value for each sample and counting the number of samples below a desired level, 95% by default. If one’s measure is above (below) the calculation corresponding to the desired level, then the measurement is distinguishable from noise. In addition, the ratio of the measurement to the calculated level provides a way to compare measurements of different degrees of freedom.
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>gofMC</artifactId> <version>1.1.2-b22</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 0.9.2644 on Jun 2, 2018.