plot,cvpen-method {quadrupen}

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.

log.scale

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

reverse

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

plot

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

main

the main title, with a hopefully appropriate default definition.

...

used for S4 compatibility.

Value

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

Examples

## 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=.2)
## Try leave one out also
cv.loo <- crossval(x,y, K=n, lambda2=0.2)


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)))