R: Computes K-fold cross-validated error curve for lars

cv.lars {lars} | R Documentation

## Computes K-fold cross-validated error curve for lars

### Description

Computes the K-fold cross-validated mean squared prediction error for lars, lasso, or forward stagewise.

### Usage

    cv.lars(x, y, K = 10, index,
        trace = FALSE, plot.it = TRUE, se = TRUE,type = c("lasso", "lar", "forward.stagewise", "stepwise"),
             mode=c("fraction", "step"), ...)

### Arguments

`x` | Input to lars
`y` | Input to lars
`K` | Number of folds
`index` | Abscissa values at which CV curve should be computed. If `mode="fraction"` this is the fraction of the saturated |beta|. The default value in this case is `index=seq(from = 0, to = 1, length =100)`. If `mode="step"`, this is the number of steps in lars procedure. The default is complex in this case, and depends on whether `N>p` or not. In principal it is `index=1:p`. Users can supply their own values of index (with care).
`trace` | Show computations?
`plot.it` | Plot it?
`se` | Include standard error bands?
`type` | type of `lars` fit, with default `"lasso"`
`mode` | This refers to the index that is used for cross-validation. The default is `"fraction"` for `type="lasso"` or `type="forward.stagewise"`. For `type="lar"` or `type="stepwise"` the default is `"step"`
`...` | Additional arguments to `lars`

### Value

Invisibly returns a list with components (which can be plotted using `plotCVlars`)

`index` | As above
`cv` | The CV curve at each value of index
`cv.error` | The standard error of the CV curve
`mode` | As above

### Author(s)

Trevor Hastie

### References

Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) Annals of Statistics; see also http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf.

### Examples

    data(diabetes)
    attach(diabetes)
    cv.lars(x2,y,trace=TRUE,max.steps=80)
    detach(diabetes)

[Package lars version 1.2 Index]