plot.cv.grpnet {adelie} | R Documentation |
plot the cross-validation curve produced by cv.grpnet
Description
Plots the cross-validation curve, and upper and lower standard deviation
curves, as a function of the lambda
values used.
Usage
## S3 method for class 'cv.grpnet'
plot(x, sign.lambda = -1, ...)
Arguments
x |
fitted |
sign.lambda |
Either plot against |
... |
Other graphical parameters |
Details
A plot is produced, and nothing is returned.
Author(s)
James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer:
Trevor Hastie hastie@stanford.edu
References
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 doi:10.48550/arXiv.2405.08631.
Adelie Python user guide https://jamesyang007.github.io/adelie/
See Also
grpnet
and cv.grpnet
.
Examples
set.seed(1010)
n = 1000
p = 100
nzc = trunc(p/10)
x = matrix(rnorm(n * p), n, p)
beta = rnorm(nzc)
fx = (x[, seq(nzc)] %*% beta)
eps = rnorm(n) * 5
y = drop(fx + eps)
px = exp(fx)
px = px/(1 + px)
ly = rbinom(n = length(px), prob = px, size = 1)
cvob1 = cv.grpnet(x, glm.gaussian(y))
plot(cvob1)
title("Gaussian Family", line = 2.5)
frame()
set.seed(1011)
cvob2 = cv.grpnet(x, glm.binomial(ly))
plot(cvob2)
title("Binomial Family", line = 2.5)
[Package adelie version 1.0.2 Index]