R: plot the cross-validation curve produced by cv.glmnet

plot.cv.glmnet {glmnet} | R Documentation

## plot the cross-validation curve produced by cv.glmnet

### Description

Plots the cross-validation curve, and upper and lower standard deviation curves, as a function of the `lambda` values used. If the object has class `"cv.relaxed"` a different plot is produced, showing both `lambda` and `gamma`

### Usage

    ## S3 method for class 'cv.glmnet'
    plot(x, sign.lambda = 1, ...)

    ## S3 method for class 'cv.relaxed'
    plot(x, se.bands = TRUE, ...)

### Arguments

`x` | fitted `"cv.glmnet"` object
`sign.lambda` | Either plot against `log(lambda)` (default) or its negative if `sign.lambda=-1`.
`...` | Other graphical parameters to plot
`se.bands` | Should shading be produced to show standard-error bands; default is `TRUE`

### Details

A plot is produced, and nothing is returned.

### Author(s)

Jerome Friedman, Trevor Hastie and Rob Tibshirani
Maintainer: Trevor Hastie hastie@stanford.edu

### References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent

### See Also

`glmnet` and `cv.glmnet`.

### 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.glmnet(x, y)
    plot(cvob1)
    title("Gaussian Family", line = 2.5)
    cvob1r = cv.glmnet(x, y, relax = TRUE)
    plot(cvob1r)
    frame()
    set.seed(1011)
    par(mfrow = c(2, 2), mar = c(4.5, 4.5, 4, 1))
    cvob2 = cv.glmnet(x, ly, family = "binomial")
    plot(cvob2)
    title("Binomial Family", line = 2.5)
    ## set.seed(1011)
    ## cvob3 = cv.glmnet(x, ly, family = "binomial", type = "class")
    ## plot(cvob3)
    ## title("Binomial Family", line = 2.5)

[Package glmnet version 4.1-2 Index]