# plot.cvpen: Plot method for cross validated error of a 'quadrupen' model In quadrupen: Sparsity by Worst-Case Quadratic Penalties

 plot,cvpen-method R Documentation

## 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)))

```

quadrupen documentation built on Jan. 16, 2023, 5:08 p.m.