quadrupen (version 0.2-4)

plot,cvpen-method: Plot method for cross validated error of a quadrupen model

Description

Produce a plot of the cross validated error of a quadrupen model.

Usage

\S4method{plot}{cvpen}(x, y, log.scale=TRUE, reverse=FALSE,
plot=TRUE, main = "Cross-validation error", ...)

Arguments

x

output of a crossval run (must be of class cvpen).

y

used for S4 compatibility.

main

the main title, with a hopefully appropriate default definition.

log.scale

logical; indicates if a log-scale should be used when xvar="lambda". Ignored for 2D cross-validation plot.

plot

logical; indicates if the graph should be plotted. Default is TRUE.

reverse

logical; should the X-axis by reversed when xvar=lambda? Default is FALSE. Ignored for 2D cross-validation plot.

...

used for S4 compatibility.

Value

a ggplot2 object which can be plotted via the print method.

Examples

## Not run: 
# ## Simulating multivariate Gaussian with blockwise correlation
# ## and piecewise constant vector of parameters
# beta <- rep(c(0,1,0,-1,0), c(25,10,25,10,25))
# cor  <- 0.75
# Soo  <- toeplitz(cor^(0:(25-1))) ## Toeplitz correlation for irrelevant variables
# Sww  <- matrix(cor,10,10) ## bloc correlation between active variables
# Sigma <- bdiag(Soo,Sww,Soo,Sww,Soo) + 0.1
# diag(Sigma) <- 1
# n <- 100
# x <- as.matrix(matrix(rnorm(95*n),n,95) %*% chol(Sigma))
# y <- 10 + x %*% beta + rnorm(n,0,10)
# 
# ## Use fewer lambda1 values by overwritting the default parameters
# ## and cross-validate over the sequences lambda1 and lambda2
# cv.double <- crossval(x,y, lambda2=10^seq(2,-2,len=50), nlambda1=50)
# ## Rerun simple cross-validation with the appropriate lambda2
# cv.10K <- crossval(x,y, lambda2=slot(cv.double, "lambda2.min"))
# ## Try leave one out also
# cv.loo <- crossval(x,y, K=n, lambda2=slot(cv.double, "lambda2.min"))
# 
# plot(cv.double)
# plot(cv.10K)
# plot(cv.loo)
# 
# ## Performance for selection purpose
# beta.min.10K <- slot(cv.10K, "beta.min")
# beta.min.loo <- slot(cv.loo, "beta.min")
# 
# cat("\nFalse positives with the minimal 10-CV choice: ", sum(sign(beta) != sign(beta.min.10K)))
# cat("\nFalse positives with the minimal LOO-CV choice: ", sum(sign(beta) != sign(beta.min.loo)))
# ## End(Not run)