The `susieR`

package implements a simple new way to perform variable selection in multiple regression (\(y=Xb+e\)). The methods implemented here are particularly well-suited to settings where some of the X variables are highly correlated, and the true effects are highly sparse (e.g. <20 non-zero effects in the vector \(b\)). One example of this is genetic fine-mapping applications, and this application was a major motivation for developing these methods. However, the methods should also be useful more generally.

The methods are based on a new model for sparse multiple regression, which we call the “Sum of Single Effects” (SuSiE) model. This model, which will be described in a manuscript in preparation (Wang et al), lends itself to a particularly simple and intuitive fitting procedure – effectively a Bayesian modification of simple forward selection, which we call “Iterative Bayesian Step-wise Selection”.

The output of the fitting procedure is a number of “Credible Sets” (CSs), which are each designed to have high probability to contain a variable with non-zero effect, while at the same time being as small as possible. You can think of the CSs as being a set of “highly correlated” variables that are each associated with the response: you can be confident that one of the variables has a non-zero coefficient, but they are too correlated to be sure which one.

The package is developed by Gao Wang, Peter Carbonetto, Yuxin Zou, Kaiqian Zhang, and Matthew Stephens from the Stephens Lab at the University of Chicago.

Please post issues to ask questions, get our support or provide us feedback; please send pull requests if you have helped fixing bugs or making improvements to the source code.

Install susieR from CRAN:

Alternatively, install the latest development version of `susieR`

from GitHub:

See here for a brief illustration of `susieR`

. For more documentation and examples please visit https://stephenslab.github.io/susieR

If you find the `susieR`

package or any of the source code in this repository useful for your work, please cite:

Wang, G., Sarkar, A., Carbonetto, P., & Stephens, M. (2020). A simple new approach to variable selection in regression, with application to genetic fine mapping.

Journal of the Royal Statistical Society, Series B82, 1273–1300. https://doi.org/10.1111/rssb.12388

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