fit_control: Control the iterations in 'fitAbn'

In abn: Modelling Multivariate Data with Additive Bayesian Networks

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.