Utility function performing Newton-Raphson algorithm updates for penLik.EMNewton
NRupdate(f, starts, gradient, hessian, ..., ridge0 = 1e-06,
tolerance = sqrt(.Machine$double.eps),
iter.max = 1500, halving.max = Inf, relative = FALSE,
return.hessian = FALSE, debugging = FALSE)Objective function to be minimized
A numeric vector of starting values
The gradient function of f
The Hessian function of f
Additional arguments to be passed to f
A small ridge factor; obsolete. Current version uses nearPD to stabilize hessian
A small numeric scalar giving the convergence criterion
Maximum number of iterations
Maximum number of step-halfing
A logical scalar indicating if relative convergence should be checked.
A logical scalar indicating if the final Hessian matrix is returned.
A logical scalar indicating if the debuggging mode of the code should be run.
A numeric vector of updated parameters, with attributes
'objective'The final evaluated objective function
'gradient'The final gradient vector
'iter'The number of iterations
'hessian'The final Hessian matrix, only available if return.hessian=TRUE.
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