quadrupen (version 0.2-4)

crossval: Cross-validation function for quadrupen fitting methods.

Description

Function that computes K-fold (double) cross-validated error of a quadrupen fit. If no lambda2 is provided, simple cross validation on the lambda1 parameter is performed. If a vector lambda2 is passed as an argument, double cross-validation is performed.

Usage

crossval(x, y, penalty = c("elastic.net", "bounded.reg"), K = 10, folds = split(sample(1:nrow(x)), rep(1:K, length = nrow(x))), lambda2 = 0.01, verbose = TRUE, mc.cores = detectCores(), ...)

Arguments

penalty

a string for the fitting procedure used for cross-validation. Either "elastic.net" or "bounded.reg", at the moment. Default is elastic.net.

x

matrix of features, possibly sparsely encoded (experimental). Do NOT include intercept.

y

response vector.

K

integer indicating the number of folds. Default is 10.

folds

list of K vectors that describes the folds to use for the cross-validation. By default, the folds are randomly sampled with the specified K. The same folds are used for each values of lambda2.

lambda2

tunes the $l2$-penalty (ridge-like) of the fit. If none is provided, the default scalar value of the corresponding fitting method is used and a simple CV is performed. If a vector of values is given, double cross-validation is performed (both on lambda1 and lambda2, using the same folds for each lambda2).

verbose

logical; indicates if the progression (the current lambda2) should be displayed. Default is TRUE.

mc.cores

the number of cores to use. The default uses all the cores available.

...

additional parameters to overwrite the defaults of the fitting procedure identified by the 'penalty' argument. See the corresponding documentation (elastic.net or bounded.reg).

Value

An object of class "cvpen" for which a plot method is available.

See Also

quadrupen, plot,cvpen-method and cvpen.

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 variable
# 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)