Utility function performing EM algorithm updates for penLik.EMNewton
EMupdate(starts, nLogLik.pen, optim.method, H, tstat, df, dt0,
spar.Pen.mat, em.iter.max = 10, em.beta.iter.max = 1,
scale.conv = 0.001, lfdr.conv = 0.001,
NPLL.conv = 0.001, debugging = FALSE)A numeric vector of starting values, in 'r' parameterization of the scale.fact
A function computing negative penalized log likelihod
One of BFGS, CG, L-BFGS-B, Nelder-Mead, SANN, nlminb, NR, the method used for optimization.
Design matrix
A numeric vector of t-statistics
A numeric scalar or vector of degrees of freedom
A numeric vector of the central t-density evaluated at the t-statistics
smoothing parameter times the penalty matrix
Maximum number of EM iterations
Maximum number of iterations in maximization step with respect to regression coefficients
A small numeric scalar specifying the convergence criterion for the scale parameter
A small numeric scalar specifying the convergence criterion for the local false discovery rates
A small numeric scalar specifying the convergence criterion for the negative penalized log likelihood
A logical scalar indicating whether debugging mode of the code should be run
A numeric vector of updated parameter estimates. The scale factor is in the log(scale.fact-1) parameterization.
Long Qu, Dan Nettleton, Jack Dekkers (2012) A hierarchical semiparametric model for incorporating inter-gene relationship information for analysis of genomic data. Biometrics, 68(4):1168-1177