| print.grpnet | R Documentation |
Print a summary of the grpnet path at each step along the path.
## S3 method for class 'grpnet'
print(x, digits = max(3, getOption("digits") - 3), ...)
x |
fitted grpnet object |
digits |
significant digits in printout |
... |
additional print arguments |
The call that produced the object x is printed, followed by a
four-column matrix with columns Groups, Df, %Dev and Lambda.
The Groups column is the number of active groups in the solution.
The Df column is the number of nonzero coefficients (Df is a
reasonable name only for lasso fits). %Dev is the percent deviance
explained (relative to the null deviance).
The matrix above is silently returned
Yang, James and Hastie, Trevor. (2024) A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent. arXiv \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2405.08631")}.
grpnet, predict, plot and coef methods.
x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
fit1 = grpnet(x, glm.gaussian(y), groups = c(1:5,7,9))
print(fit1)
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