Processes | Predictive Modeling | Genomic Bayesian Regression

Genomic Bayesian Regression
The Genomic Bayesian Regression process builds predictive models using the Bayesian methods present in the BGLR package (Pérez and de los Campos, 20131). Genomic Bayesian regression is a form of regularized regression that allows for numerous, potentially correlated, predictors and shrinks them using a common variance component model. A complete guide to the BGLR package found at
What do I need?
You must have the latest version of base R installed on your machine. Refer to The R Project for Statistical Computing for downloads and more information. You must also download BGLR package. Use the R Package Manager to download and add this and other packages to your base R installation. You may also need to modify the sasv9.cfg file. Refer to the R Package Manager documentation for instructions.
One SAS data set is required: An input data set with one column per predictor variable (feature) and response variable (target).
The output generated by this process is summarized in a Tabbed report. Refer to the Genomic Bayesian Regression output documentation for detailed descriptions and guides to interpreting your results.

Pérez P, de los Campos G. BGLR: a statistical package for whole genome regression and prediction. R package version 1. 0.2. 2013.