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NLL

Evaluate the (penalized) (fused) likelihood

Description

Functions that evaluate the (penalized) (fused) likelihood.

Usage

NLL(S, P)

PNLL(S, P, T, lambda)

NLL.fused(Slist, Plist, ns)

PNLL.fused(Slist, Plist, ns, Tlist, lambda)

Arguments

S, Slist

A (list of) positive semi definite sample covariance matrices.
P, Plist

A (list of) positive definite precision matrices.
T, Tlist

A (list of) positive definite target matrices.
lambda

A numeric penalty parameter. For the .fused functions, this is a penalty matrix.
ns

A numeric of sample sizes.

Value

A single number.

Author(s)

Anders Ellern Bilgrau, Carel F.W. Peeters <cf.peeters@vumc.nl>, Wessel N. van Wieringen

See Also

Examples

ns <- c(4,5)
Slist <- createS(n = ns, p = 5)
Plist <- list(diag(5), diag(2,5))
Tlist <- list(diag(5), diag(5))

NLL(Slist[[1]], Plist[[1]])
PNLL(Slist[[1]], Plist[[1]], Tlist[[1]], lambda = 1)
NLL.fused(Slist, Plist, ns)
PNLL.fused(Slist, Plist, ns, Tlist, lambda = diag(2))
rags2ridges

Ridge Estimation of Precision Matrices from High-Dimensional Data

v2.2.4
GPL (>= 2)
Authors
Carel F.W. Peeters [cre, aut], Anders Ellern Bilgrau [aut], Wessel N. van Wieringen [aut]
Initial release

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