sp.gwas-package: Selection probabilities using generalized linear model with...
In statpng/sp.gwas: An integrated tool for an analysis of high-dimensional genomic data in Genome-Wide Association Study
Description
Details
Value
References
Description
sp.gwas
Details
The penalty function of elastic-net is defined as
α||β||_1+(1-α)||β||_2/2,
where α is a mixing proportion of ridge and the lasso, and β is regression coefficients. This penalty is equivalent to the Lasso penalty if alpha=1.
Value
A list of data files(genotype, phenotype, etc.), results for selection probabilities, and manhattan plot for multiple traits.
References
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology), 67(2), 301-320.
statpng/sp.gwas documentation built on Dec. 17, 2020, 5:55 a.m.
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Description Details Value References
Description
sp.gwas
Details
The penalty function of elastic-net is defined as
α||β||_1+(1-α)||β||_2/2,
where α is a mixing proportion of ridge and the lasso, and β is regression coefficients. This penalty is equivalent to the Lasso penalty if alpha=1.
Value
A list of data files(genotype, phenotype, etc.), results for selection probabilities, and manhattan plot for multiple traits.
References
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology), 67(2), 301-320.
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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