ADMMc: Penalized precision matrix estimation via ADMM (c++)

Description Usage Arguments Details Value Author(s) References

View source: R/RcppExports.R

Description

Penalized precision matrix estimation using the ADMM algorithm

Usage

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ADMMc(S, A, B, C, initOmega, initZ, initY, lam, alpha = 1, tau = 10,
  rho = 2, mu = 10, tau_rho = 2, iter_rho = 10L, crit = "ADMM",
  tol_abs = 1e-04, tol_rel = 1e-04, maxit = 10000L)

Arguments

S

pxp sample covariance matrix (denominator n).

A

option to provide user-specified matrix for penalty term. This matrix must have p columns. Defaults to identity matrix.

B

option to provide user-specified matrix for penalty term. This matrix must have p rows. Defaults to identity matrix.

C

option to provide user-specified matrix for penalty term. This matrix must have nrow(A) rows and ncol(B) columns. Defaults to identity matrix.

initOmega

initialization matrix for Omega

initZ

initialization matrix for Z2

initY

initialization matrix for Y

lam

postive tuning parameter for elastic net penalty.

alpha

elastic net mixing parameter contained in [0, 1]. 0 = ridge, 1 = lasso. Alpha must be a single value (cross validation across alpha not supported).

tau

optional constant used to ensure positive definiteness in Q matrix in algorithm

rho

initial step size for ADMM algorithm.

mu

factor for primal and residual norms in the ADMM algorithm. This will be used to adjust the step size rho after each iteration.

tau_rho

factor in which to increase step size rho.

iter_rho

step size rho will be updated every iter.rho steps

crit

criterion for convergence (ADMM or loglik). If crit = loglik then iterations will stop when the relative change in log-likelihood is less than tol.abs. Default is ADMM and follows the procedure outlined in Boyd, et al.

tol_abs

absolute convergence tolerance. Defaults to 1e-4.

tol_rel

relative convergence tolerance. Defaults to 1e-4.

maxit

maximum number of iterations. Defaults to 1e4.

Details

For details on the implementation of 'ADMMsigma', see the vignette https://mgallow.github.io/SCPME/.

Value

returns list of returns which includes:

Iterations

number of iterations.

lam

optimal tuning parameter.

Omega

estimated penalized precision matrix.

Z2

estimated Z matrix.

Y

estimated Y matrix.

rho

estimated rho.

Author(s)

Matt Galloway gall0441@umn.edu

References


SCPME documentation built on May 2, 2019, 3:42 a.m.