fit_control: Control the iterations in 'fitAbn'

fit.controlR Documentation

Control the iterations in fitAbn

Description

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

Usage

fit.control(method = "bayes", mean = 0, prec = 0.001, loggam.shape = 1,
            loggam.inv.scale = 5e-05, max.mode.error = 10, 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 = 1e-04, factor.brent = 100, maxiters.hessian.brent = 10,
            num.intervals.brent = 100, min.pdf = 0.001, n.grid = 250, std.area = TRUE,
            marginal.quantiles = c(0.025, 0.25, 0.5, 0.75, 0.975), max.grid.iter = 1000,
            marginal.node = NULL, marginal.param = NULL, variate.vec = NULL,
            max.irls = 100, tol = 10^-11, seed = 9062019)

Arguments

method

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

mean

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

prec

the prior precision for all the Gaussian additive terms for each node.

loggam.shape

the shape parameter in the Gamma distributed prior for the precision in any Gaussian nodes, also used for group-level precision is applicable.

loggam.inv.scale

the inverse scale parameter in the Gamma distributed prior for the precision in any Gaussian nodes, also used for group-level precision, is applicable.

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. See details.

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 step size 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, see details

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

min.pdf

the value of the posterior density function below which we stop the estimation only used when computing marginals, see details.

n.grid

recompute density on an equally spaced grid with n.grid points.

std.area

logical, should the area under the estimated posterior density be standardized to exactly one, useful for error checking.

marginal.quantiles

vector giving quantiles at which to compute the posterior marginal distribution at.

max.grid.iter

gives number of grid points to estimate posterior density at when not explicitly specifying a grid used to avoid excessively long computation.

marginal.node

used in conjunction with marginal.param to allow bespoke estimate of a marginal density over a specific grid. value from 1 to the number of nodes.

marginal.param

used in conjunction with marginal.node. value of 1 is for intercept, see modes entry in results for the appropriate number.

variate.vec

a vector containing the places to evaluate the posterior marginal density, must be supplied if marginal.node is not null

max.irls

integer given the maximum number of run 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.

seed

a non-negative integer which sets the seed.

Value

A list with 26 components for the Bayesian approach, or a list with 3 components for "mle".

Examples

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

ctrlbayes <- fit.control(method = "bayes", mean = 0, prec = 0.001, loggam.shape = 1,
  loggam.inv.scale = 5e-05, max.mode.error = 10, 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 = 1e-04, factor.brent = 100,
  maxiters.hessian.brent = 10, num.intervals.brent = 100, min.pdf = 0.001,
  n.grid = 100, std.area = TRUE, marginal.quantiles = c(0.025, 0.25, 0.5, 0.75, 0.975),
  max.grid.iter = 1000, marginal.node = NULL, marginal.param = NULL, variate.vec = NULL,
  seed = 9062019)

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