Description Usage Arguments Value Examples
Compute the best condition number regularized based based on cross-validation selected penalty parameter
1 | select_condreg(X, k, ...)
|
X |
n-by-p matrix of data |
k |
vector of penalties for cross-validation |
... |
parameters for |
list of condition number regularized covariance matrix S and its inverse invS
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## True covariance matrix
sigma <- diag(5)
sigma[3,2] <- sigma[2,3] <- 0.8
## Generate normal random samples
## Not run:
library(MASS)
X <- mvrnorm(200,rep(0,5),sigma)
## Covariance estimation
gridpts <- kgrid(50,100) ## generate grid of penalties to search over
crcov <- select_condreg(X,gridpts) ## automatically selects penalty parameter
## Inspect output
str(crcov) ## returned object
sigma.hat <- crcov$S ## estimate of sigma matrix
omega.hat <- crcov$invS ## estimate of inverse of sigma matrix
## End(Not run)
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