plot.flamCV {flam} | R Documentation |
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.
## S3 method for class 'flamCV' plot(x, showSE = T, ...)
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.
#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)