README.md
In PNNL-Comp-Mass-Spec/glmnetGLR: glmnetGLR
glmnetGLR
Abstract
The goal of this project was to build a classifier that accurately predicts a binary response.
The resulting solution uses a Lasso logistic regression classifier (LLRC), which simultaneously restricts
the size of model coefficients and the number of quality metrics included the model.
Importantly the model does not treat false-negatives and false-positives as equally deleterious events.
Usage
See the PDF file glmnetGLR.pdf in the doc folder (below the inst folder).\
Available online at https://github.com/PNNL-Comp-Mass-Spec/glmnetGLR/blob/master/inst/doc/glmnetGLR.pdf
License
See the LICENSE file
PNNL-Comp-Mass-Spec/glmnetGLR documentation built on May 28, 2019, 2:23 p.m.
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glmnetGLR
Abstract
The goal of this project was to build a classifier that accurately predicts a binary response. The resulting solution uses a Lasso logistic regression classifier (LLRC), which simultaneously restricts the size of model coefficients and the number of quality metrics included the model. Importantly the model does not treat false-negatives and false-positives as equally deleterious events.
Usage
See the PDF file glmnetGLR.pdf in the doc folder (below the inst folder).\ Available online at https://github.com/PNNL-Comp-Mass-Spec/glmnetGLR/blob/master/inst/doc/glmnetGLR.pdf
License
See the LICENSE file
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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