Description Usage Arguments References See Also Examples

`cernn`

performs stable covariance estimation over a grid of regularization parameters.

1 | ```
cernn(X, lambda, alpha)
``` |

`X` |
The data matrix whose rows are observations and columns are covariates. |

`lambda` |
vector of regularization parameters controling amount of shrinkage towards the target. |

`alpha` |
Parameter that controls mixture between the trace and inverse trace penalties. |

Eric C. Chi and Kenneth Lange, Stable estimation of a covariance matrix guided by nuclear norm penalties, Computational Statistics and Data Analysis, 80:117-128, 2014.

`get_alpha`

, `shrink_eigen`

, `select_lambda`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
n <- 10
p <- 5
set.seed(12345)
X <- matrix(rnorm(n*p),n,p)
alpha <- get_alpha(X)
lambda <- 10**(seq(-1,4,length.out=100))
sol_path <- cernn(X,lambda,alpha)
df <- t(sol_path$e)
## Plot regularization paths of eigenvalues
matplot(x=log10(lambda),y=df,type='l',ylab='shrunken eigenvalue')
grand_mean <- (norm(scale(X,center=TRUE,scale=FALSE),'f')**2)/(n*p)
abline(h=grand_mean)
``` |

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