hierband.path: Solves main optimization problem over a grid of lambda values
In hierband: Convex Banding of the Covariance Matrix

Description Usage Arguments Value See Also Examples

See hierband for the problem this is solving. If lamlist not provided, then grid will be constructed starting at lambda_max, the smallest value of lam for which the solution (with delta=NULL) is diagonal.

1 2	hierband.path(Sighat, nlam = 20, flmin = 0.01, lamlist = NULL, w = NULL, delta = NULL, maxiter = 100, tol = 1e-07)

`Sighat`	The sample covariance matrix
`nlam`	Number of lambda values to include in grid.
`flmin`	Ratio between the smallest lambda and largest lambda in grid. (Default: 0.01) Decreasing this gives less sparse solutions.
`lamlist`	A grid of lambda values to use. If this is non-NULL, then `nlam` and `flmin` are ignored.
`w`	`(p-1)`-by-`(p-1)` lower-triangular matrix (above diagonal ignored). `w[l,]` gives the `l` weights for g_l. Defaults to `w[l,m]=sqrt(2 * l)/(l - m + 1)` for `m <= l`
`delta`	Lower bound on eigenvalues. If this is NULL (which is default), then no eigenvalue constraint is included.
`maxiter`	Number of iterations of blockwise coordinate descent to perform.
`tol`	Only used when `delta` is non-NULL. When no eigenvalue changes by more than `tol` in BCD, convergence is assumed.

Returns a sequence of convex banded estimates of the covariance matrix.

P:: A nrow(Sighat)-by-nrow(Sighat)-by-nlam array where P[, , i] gives the ith estimate of the covariance matrix.
lamlist:: Grid of lambda values used.
w:: Value of w used.
delta:: Value of delta used.

hierband hierband.cv

set.seed(123)
p <- 100
n <- 50
K <- 10
true <- ma(p, K)
x <- matrix(rnorm(n*p), n, p) %*% true$A
Sighat <- cov(x)
path <- hierband.path(Sighat)
cv <- hierband.cv(path, x)
fit <- hierband(Sighat, lam=cv$lam.best)