# plot.flamcv: Plots Cross-Validation Curve for Object of Class "flamCV" In flam: Fits Piecewise Constant Models with Data-Adaptive Knots

## Description

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.

## Usage

 ```1 2``` ```## S3 method for class 'flamCV' plot(x, showSE = T, ...) ```

## Arguments

 `x` an object of class "flamCV". `showSE` a logical (`TRUE` or `FALSE`) for whether the standard errors of the curve should be plotted. `...` additional arguments to be passed. These are ignored in this function.

Ashley Petersen

## References

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

`flamCV`
 ``` 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) ```