Description Usage Arguments Author(s) References See Also Examples

This function plots the cross-validation curve for a series of models fit using `flamCV`

. The cross-validation error with +/-1 standard error is plotted for each value of lambda considered in the call to `flamCV`

with a dotted vertical line indicating the chosen lambda.

1 2 |

`x` |
an object of class "flamCV". |

`showSE` |
a logical ( |

`...` |
additional arguments to be passed. These are ignored in this function. |

Ashley Petersen

Petersen, A., Witten, D., and Simon, N. (2014). Fused Lasso Additive Model. arXiv preprint arXiv:1409.5391.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
#See ?'flam-package' for a full example of how to use this package
#generate data
set.seed(1)
data <- sim.data(n = 50, scenario = 1, zerof = 0, noise = 1)
#fit model and select tuning parameters using 2-fold cross-validation
#note: use larger 'n.fold' (e.g., 10) in practice
flamCV.out <- flamCV(x = data$x, y = data$y, within1SE = TRUE, n.fold = 2)
#lambdas chosen is
flamCV.out$lambda.cv
#we can now plot the cross-validation error curve with standard errors
#vertical dotted line at lambda chosen by cross-validation
plot(flamCV.out)
#or without standard errors
plot(flamCV.out, showSE = FALSE)
## Not run:
#can choose lambda to be value with minimum CV error
#instead of lambda with CV error within 1 standard error of the minimum
flamCV.out2 <- flamCV(x = data$x, y = data$y, within1SE = FALSE, n.fold = 2)
#contrast to chosen lambda for minimum cross-validation error
#it's a less-regularized model (i.e., lambda is smaller)
plot(flamCV.out2)
## End(Not run)
``` |

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