Learn R Programming

SFSI (version 1.2.0)

Sparse Family and Selection Index

Description

Here we provide tools for the estimation of coefficients in penalized regressions when the (co)variance matrix of predictors and the covariance vector between predictors and response, are provided. These methods are extended to the context of a Selection Index (commonly used for breeding value prediction). The approaches offer opportunities such as the integration of high-throughput traits in genetic evaluations ('Lopez-Cruz et al., 2020') and solutions for training set optimization in Genomic Prediction ('Lopez-Cruz & de los Campos, 2021') .

Copy Link

Version

Install

install.packages('SFSI')

Monthly Downloads

107

Version

1.2.0

License

GPL-3

Maintainer

Marco Lopez-Cruz

Last Published

August 16th, 2022

Functions in SFSI (1.2.0)

path.plot

Coefficients path plot
solveEN

Coordinate Descent algorithm to solve Elastic-Net-type problems
getGenCov

Genetic covariances
covariance_matrix

Conversion of a covariance matrix to a distance or correlation matrix
Methods_SSI

SSI methods
collect

collect function
wheat

Wheat dataset
SSI

Sparse Selection Index
Methods_LASSO

LASSO methods
BinaryFiles

Binary files
net.plot

Network plot
fitBLUP

Function fitBLUP
LARS

Least Angle Regression to solve LASSO-type problems