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

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

`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

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

flamCV

#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)