random.CV: Select Tuning Parameter for Regularized Covariance Matrix by...
In CVTuningCov: Regularized Estimators of Covariance Matrices with CV Tuning

Description Usage Arguments Value Author(s) Examples

View source: R/functions.R

Apply a random cross-validation (CV) to select tuning parameters for regualrized covariance matrix with banding, tapering, soft-thresholding or hard-thresholding method under the Frobenius norm or the operator norm. The random CV randomly splits the data set to two parts, a training set and a validation set with user-specifed sizes, and repeats the process for multiple times.

1 2	random.CV(X, k.grid = 0.5, method = "Tapering", test.size = 5, norm = "F", boot.num = 50, seed = 10323)

`X`	input data matrix with dimension `n*p`. `n` indicates the sample size and `p` indicates the dimension of the corresponding random vector.
`k.grid`	the default value is 0.5.
`method`	the regularized method, which can be "Banding", "Tapering", "HardThresholding" or "SoftThresholding". the default value is "Tapering".
`test.size`	the size of the validation set, which should be `< n`.
`norm`	the norms which can be used to measure the estimation accuracy, which can be the Frobenius norm "F" or the operator norm "L2".
`boot.num`	the number of random split. The default value is 50.
`seed`	the default value is 10323.

A list including elements:

`CV.k`	the optimal tuning parameter selected by the random CV.
`k.grid`	the vector of tuning parameters
`CV.pre.error`	a vector denoting predicting errors by random CV at each element of tuning parameters based on the selected norm.

Binhuan Wang

library(MASS);
n <- 50;
p <- 50;
fold <- 3;
k.grid <- seq(0,2*(p-1),by=1);
Sigma <- AR1(p, rho=0.6);
X <- mvrnorm(n,rep(0,p),Sigma);
CV.F.fit <- random.CV(X,k.grid, method='Tapering',test.size = 10,norm='F');
CV.F.fit$CV.k;