The easiest way to run a single method for a single observation at a single site is to use the script scratch/beauty-atomic-scratch.r, modifying that script's first section to run the contest on the appropriate data. The main part of the contest was run on a massively parallel environment via Condor. That mani run was sent out to the compute nodes via the shell script chtc/boot.sh, which calls chtc/Makefile to do the work of sending out jobs.

We wanted to see which regression technique performs best for modeling the concentration of E. coli in beach water. Fourteen settings were tested (using seven modules - several modules had optional settings that were counted as new settings) at seven sites in Wisconsin. Eash site was modeled with each setting and the performance was compared.

These are the modules for the contest:

• gbm Gradient moosting machine
• pls Partial least squares
• adapt Adaptive lasso (for gaussian response)
• adalasso Adaptive lasso (for logistic regression)
• galm Genetic algorithm for gaussian response
• galogistic Genetic algorithm for logistic regression
• spls Sparse partial least squares (combines lasso and PLS)

Data for the contest were collected either manually or automatically. Automatic collection was the majority, and came from the USGS' EnDDaT and GeoDataPortal tools. Manual collection was driven by the EPA's Beach Sanitary Surveys.

Our models are used to drive a decision of whether or not to post a warning at the beach. Therefore, we are ultimately interested in which method most accurately classified its predictions as exceedances or nonexceedances. For the linear regression techniques, accurately predicting the actual concentration was important, too. In the end, there were three performance measures:

• Area under the ROC curve (AUROC - measures how well a model sorted exceedances from nonexceedances
• Predictive error sum of squares (PRESS) - measures the total squared error of the model's predictions
• Classification of responses - counts of the true/false positives and true/false negatives from the model's predictions

The classification of responses is like AUROC, but for a specific choice of threshold rather than averaged over the possible thresholds.

The scripts to do the work of the contest are in the R/ directory. Secifically, R/beauty-atomic and R/annual-atomic manage the dirty work of fitting models and making predictions from them. That dirty work is actually done in the modules, which are R scripts under the names given in the list above. There is a common interface to the modules: the required methods are Create and Predict.