getmarginals: Internal function called by 'fitAbn.bayes'.

In abn: Modelling Multivariate Data with Additive Bayesian Networks

Internal function called by fitAbn.bayes.

Description

Function for computing marginal posterior densities using C and is called from fit.dag() Only to be called internally.

Usage

getmarginals(
  res.list,
  data.df,
  dag.m,
  var.types,
  max.parents,
  mean,
  prec,
  loggam.shape,
  loggam.inv.scale,
  max.iters,
  epsabs,
  verbose,
  error.verbose,
  trace,
  grouped.vars,
  group.ids,
  epsabs.inner,
  max.iters.inner,
  finite.step.size,
  hessian.params,
  max.iters.hessian,
  min.pdf,
  marginal.node,
  marginal.param,
  variate.vec,
  n.grid,
  INLA.marginals,
  iter.max,
  max.hessian.error,
  factor.brent,
  maxiters.hessian.brent,
  num.intervals.brent
)

Arguments

res.list	rest of arguments as for call to C fitabn
data.df	a data frame containing the data used for learning the network, binary variables must be declared as factors, and no missing values all allowed in any variable.
dag.m	adjacency matrix
var.types	distributions in terms of a numeric code
max.parents	max number of parents over all nodes in dag (different from other max.parents definitions).
mean	the prior mean for all the Gaussian additive terms for each node. INLA argument control.fixed=list(mean.intercept=...) and control.fixed=list(mean=...).
prec	the prior precision (\tau = \frac{1}{\sigma^2}) for all the Gaussian additive term for each node. INLA argument control.fixed=list(prec.intercept=...) and control.fixed=list(prec=...).
loggam.shape	the shape parameter in the Gamma distribution prior for the precision in a Gaussian node. INLA argument control.family=list(hyper = list(prec = list(prior="loggamma",param=c(loggam.shape, loggam.inv.scale)))).
loggam.inv.scale	the inverse scale parameter in the Gamma distribution prior for the precision in a Gaussian node. INLA argument control.family=list(hyper = list(prec = list(prior="loggamma",param=c(loggam.shape, loggam.inv.scale)))).
max.iters	total number of iterations allowed when estimating the modes in Laplace approximation. passed to .Call("fit_single_node", ...).
epsabs	absolute error when estimating the modes in Laplace approximation for models with no random effects. Passed to .Call("fit_single_node", ...).
verbose	if TRUE then provides some additional output, in particular the code used to call INLA, if applicable.
error.verbose	logical, additional output in the case of errors occurring in the optimization. Passed to .Call("fit_single_node", ...).
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.). Passed to .Call("fit_single_node", ...).
grouped.vars	result returned from check.valid.groups. Column indexes of all variables which are affected from grouping effect.
group.ids	result returned from check.valid.groups. Vector of group allocation for each observation (row) in 'data.df'.
epsabs.inner	absolute error in the maximization step in the (nested) Laplace approximation for each random effect term. Passed to .Call("fit_single_node", ...).
max.iters.inner	total number of iterations in the maximization step in the nested Laplace approximation. Passed to .Call("fit_single_node", ...).
finite.step.size	suggested step length used in finite difference estimation of the derivatives for the (outer) Laplace approximation when estimating modes. Passed to .Call("fit_single_node", ...).
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. Passed to .Call("fit_single_node", ...).
max.iters.hessian	integer, maximum number of iterations to use when determining an optimal finite difference approximation (Nelder-Mead). Passed to .Call("fit_single_node", ...).
min.pdf	the value of the posterior density function below which we stop the estimation only used when computing marginals, see details.
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.
n.grid	recompute density on an equally spaced grid with n.grid points.
INLA.marginals	vector - TRUE if INLA used false otherwise
iter.max	same as max.iters in fit.control. Total number of iterations allowed when estimating the modes in Laplace approximation. Passed to .Call("fit_single_node", ...).
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. Passed to .Call("fit_single_node", ...).
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). Passed to .Call("fit_single_node", ...).
maxiters.hessian.brent	maximum number of iterations allowed in the Brent-Dekker method. Passed to .Call("fit_single_node", ...).
num.intervals.brent	the number of initial different bracket segments to try in the Brent-Dekker method. Passed to .Call("fit_single_node", ...).

See Also

Other Bayes: buildScoreCache(), calc.node.inla.glmm(), calc.node.inla.glm(), fitAbn()

abn documentation built on Nov. 3, 2023, 5:08 p.m.