plot.sparseSVM: Plot coefficients from a "sparseSVM" object

In sparseSVM: Solution Paths of Sparse High-Dimensional Support Vector Machine with Lasso or Elastic-Net Regularization

Description

Produce a plot of the coefficient paths for a fitted "sparseSVM" object.

Usage

## S3 method for class 'sparseSVM'
plot(x, xvar = c("lambda", "norm"), log.l = TRUE, nvars = TRUE, 
    alpha = 1, ...)

Arguments

x	A sparseSVM object.
xvar	What is on the X-axis. "lambda" plots against the lambda sequence, "norm" against the L1-norm of the coefficients. Default is "lambda".
log.l	Should log(lambda) be used instead of lambda when xvar = "lambda"? Default is TRUE. It has no effect on "norm".
nvars	If TRUE (the default), places an axis on top of the plot denoting the number of variables with nonzero coefficients at each lambda.
alpha	A value between 0 and 1 for alpha transparency channel(0 means transparent and 1 means opaque), helpful when the number of variables is large.
...	Other graphical parameters to plot.

Author(s)

Congrui Yi and Yaohui Zeng
Maintainer: Congrui Yi <[e-mail]>

See Also

sparseSVM

Examples

X = matrix(rnorm(1000*100), 1000, 100)
b = 3
w = 5*rnorm(10)
eps = rnorm(1000)
y = sign(b + drop(X[,1:10] %*% w + eps))

fit = sparseSVM(X, y)
par(mfrow = c(2,2))
plot(fit)
plot(fit, nvars = FALSE, alpha = 0.5)
plot(fit, log.l = FALSE)
plot(fit, xvar = "norm")

Example output

sparseSVM documentation built on May 2, 2019, 11:02 a.m.