build_control: Control the iterations in 'buildScoreCache'

build.controlR Documentation

Control the iterations in buildScoreCache

Description

Allow the user to set restrictions in the buildscorecache for both the Bayesian and the MLE approach.

Usage

build.control(method = "bayes", max.mode.error = 10, mean = 0, prec = 0.001,
          loggam.shape = 1, loggam.inv.scale = 5e-05, max.iters = 100, epsabs = 1e-07,
          error.verbose = FALSE, trace = 0L, epsabs.inner = 1e-06, max.iters.inner = 100,
          finite.step.size = 1e-07, hessian.params = c(1e-04, 0.01),
          max.iters.hessian = 10, max.hessian.error = 0.5, factor.brent = 100,
          maxiters.hessian.brent = 100, num.intervals.brent = 100,
          ncores = 0, max.irls = 100, tol = 10^-8, seed = 9062019)

Arguments

method

a character that takes one of two values: "bayes" or "mle"

max.mode.error

if the estimated modes from INLA differ by a factor of max.mode.error or more from those computed internally, then results from INLA are replaced by those computed internally. To force INLA always to be used, then max.mode.error=100, to force INLA never to be used max.mod.error=0.

mean

the prior mean for all the Gaussian additive terms for each node

prec

the prior precision for all the Gaussian additive term for each node

loggam.shape

the shape parameter in the Gamma distribution prior for the precision in a Gaussian node

loggam.inv.scale

the inverse scale parameter in the Gamma distribution prior for the precision in a Gaussian node

max.iters

total number of iterations allowed when estimating the modes in Laplace approximation

epsabs

absolute error when estimating the modes in Laplace approximation for models with no random effects.

error.verbose

logical, additional output in the case of errors occurring in the optimization

trace

Non-negative integer. If positive, tracing information on the progress of the "L-BFGS-B" optimization is produced. Higher values may produce more tracing information. (There are six levels of tracing. To understand exactly what these do see the source code.)

epsabs.inner

absolute error in the maximization step in the (nested) Laplace approximation for each random effect term

max.iters.inner

total number of iterations in the maximization step in the nested Laplace approximation

finite.step.size

suggested step length used in finite difference estimation of the derivatives for the (outer) Laplace approximation when estimating modes

hessian.params

a numeric vector giving parameters for the adaptive algorithm, which determines the optimal stepsize in the finite-difference estimation of the hessian. First entry is the initial guess, second entry absolute error

max.iters.hessian

integer, maximum number of iterations to use when determining an optimal finite difference approximation (Nelder-Mead)

max.hessian.error

if the estimated log marginal likelihood when using an adaptive 5pt finite-difference rule for the Hessian differs by more than max.hessian.error from when using an adaptive 3pt rule then continue to minimize the local error by switching to the Brent-Dekker root bracketing method

factor.brent

if using Brent-Dekker root bracketing method then define the outer most interval end points as the best estimate of h (stepsize) from the Nelder-Mead as (h/factor.brent,h*factor.brent)

maxiters.hessian.brent

maximum number of iterations allowed in the Brent-Dekker method

num.intervals.brent

the number of initial different bracket segments to try in the Brent-Dekker method

max.irls

total number of iterations for estimating network scores using an Iterative Reweighed Least Square algorithm

tol

real number giving the minimal tolerance expected to terminate the Iterative Reweighed Least Square algorithm to estimate network score.

ncores

The number of cores to parallelize to, see ‘Details’.

seed

a non-negative integer which sets the seed.

Details

Parallelization over all children is possible via the function foreach of the package doParallel. ncode=1 uses single threaded foreach. ncode=-1 uses all available cores but one.

With ncores=0 a simple for loop is used.

Value

A list with 18 components for the Bayesian approach, or a list with 4 components for "mle"

Examples

ctrlmle <- build.control(method = "mle", ncores = 0, max.irls = 100, tol = 10^-11, seed = 9062019)

ctrlbayes <- build.control(method = "bayes", max.mode.error = 10, mean = 0, prec = 0.001,
   loggam.shape = 1, loggam.inv.scale = 5e-05, max.iters = 100,
   epsabs = 1e-07, error.verbose = FALSE, epsabs.inner = 1e-06,
   max.iters.inner = 100, finite.step.size = 1e-07, hessian.params = c(1e-04, 0.01),
   max.iters.hessian = 10, max.hessian.error = 0.5, factor.brent = 100,
   maxiters.hessian.brent = 100, num.intervals.brent = 100,
   tol = 10^-8, seed = 9062019)



abn documentation built on April 25, 2022, 9:06 a.m.