R: Plots the cross-validation curve from a 'cv.grpreg' object

plot.cv.grpreg {grpreg} | R Documentation

## Plots the cross-validation curve from a `cv.grpreg` object

### Description

Plots the cross-validation curve from a `cv.grpreg` object, along with standard error bars.

### Usage

    ## S3 method for class 'cv.grpreg'
    plot(x, log.l=TRUE, type=c("cve", "rsq", "scale",
    "snr", "pred", "all"),  selected=TRUE, vertical.line=TRUE, col="red",
    ...)

### Arguments

`x` | A `cv.grpreg` object.
`log.l` | Should horizontal axis be on the log scale? Default is TRUE.
`type` | What to plot on the vertical axis. `cve` plots the cross-validation error (deviance); `rsq` plots an estimate of the fraction of the deviance explained by the model (R-squared); `snr` plots an estimate of the signal-to-noise ratio; `scale` plots, for `family="gaussian"`, an estimate of the scale parameter (standard deviation); `pred` plots, for `family="binomial"`, the estimated prediction error; `all` produces all of the above.
`selected` | If `TRUE` (the default), places an axis on top of the plot denoting the number of groups in the model (i.e., that contain a nonzero regression coefficient) at that value of `lambda`.
`vertical.line` | If `TRUE` (the default), draws a vertical line at the value where cross-validaton error is minimized.
`col` | Controls the color of the dots (CV estimates).
`...` | Other graphical parameters to `plot`

### Details

Error bars representing approximate +/- 1 SE (68% confidence intervals) are plotted along with the estimates at value of `lambda`. For `rsq` and `snr`, these confidence intervals are quite crude, especially near zero, and will hopefully be improved upon in later versions of `grpreg`.

### Author(s)

Patrick Breheny <patrick-breheny@uiowa.edu>

### See Also

`grpreg`, `cv.grpreg`

### Examples

    # Birthweight data
    data(Birthwt)
    X <- Birthwt$X
    group <- Birthwt$group

    # Linear regression
    y <- Birthwt$bwt
    cvfit <- cv.grpreg(X, y, group)
    plot(cvfit)
    par(mfrow=c(2,2))
    plot(cvfit, type="all")

    ## Logistic regression
    y <- Birthwt$low
    cvfit <- cv.grpreg(X, y, group, family="binomial")
    plot(cvfit)
    par(mfrow=c(2,2))
    plot(cvfit, type="all")

[Package grpreg version 3.0-2 Index]