plot,cvpen-method | R Documentation |
quadrupen
modelProduce a plot of the cross validated error of a quadrupen
model.
\S4method{plot}{cvpen}(x, y, log.scale=TRUE, reverse=FALSE, plot=TRUE, main = "Cross-validation error", ...)
x |
output of a |
y |
used for S4 compatibility. |
log.scale |
logical; indicates if a log-scale should be used
when |
reverse |
logical; should the X-axis by reversed when |
plot |
logical; indicates if the graph should be plotted. Default is |
main |
the main title, with a hopefully appropriate default definition. |
... |
used for S4 compatibility. |
a ggplot2 object which can be plotted via the print
method.
## 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)))
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