control.inla: Control variables in control.inla

In andrewzm/INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion

Description

Control variables in control.inla for use in inla

Usage

inla.set.control.inla.default(...)
control.inla(adapt.hessian.max.trials, adapt.hessian.mode, adapt.hessian.scale, adjust.weights, cmin, correct, correct.factor, correct.strategy, correct.verbose, cpo.diff, cutoff, diagonal, diff.logdens, dz, fast, force.diagonal, global.node.degree, global.node.factor, h, huge, int.strategy, interpolator, lincomb.derived.correlation.matrix, lincomb.derived.only, linear.correction, mode.known, npoints, numint.abserr, numint.maxfeval, numint.relerr, optimiser, print.joint.hyper, reordering, restart, skip.configurations, stencil, step.factor, step.len, strategy, stupid.search, stupid.search.factor, stupid.search.max.iter, tolerance, tolerance.f, tolerance.g, tolerance.x, verbose)

Arguments

...	Possible arguments
strategy	The strategy to use for the approximations; one of 'gaussian', 'simplified.laplace' (default) or 'laplace'
int.strategy	The integration strategy to use; one of 'ccd' (default), 'grid' or 'eb' (empirical bayes)
interpolator	The interpolator used to compute the marginals for the hyperparameters. One of 'auto', 'nearest', 'quadratic', 'weighted.distance', 'ccd', 'ccdintegrate', 'gridsum', 'gaussian'. Default is 'auto'.
fast	Fast mode? If on, then replace conditional modes in the Laplace approximation with conditional expectation (default TRUE)
linear.correction	Default TRUE for the 'strategy = laplace' option.
h	The step-length for the gradient calculations for the hyperparameters. Default 0.01.
dz	The step-length in the standarised scale for the integration of the hyperparameters. Default 1.0.
diff.logdens	The difference of the log.density for the hyperpameters to stop numerical integration using int.strategy='grid'. Default 2.5.
print.joint.hyper	If TRUE, the store also the joint distribution of the hyperparameters (without any costs). Default TRUE.
force.diagonal	A boolean variable, if TRUE, then force the Hessian to be diagonal. (Default FALSE.)
skip.configurations	A boolean variable; skip configurations if the values at the main axis are to small. (Default TRUE.)
mode.known	A boolean variable: If TRUE then no optimisation is done. (Default FALSE.)
adjust.weights	A boolean variable; If TRUE then just more accurate integration weights. (Default TRUE.)
tolerance	The tolerance for the optimisation of the hyperparameters. If set, this is the default value for for 'tolerance.f^(2/3)', 'tolerance.g' and 'tolerance.x'; see below.
tolerance.f	The tolerance for the absolute change in the log posterior in the optimisation of the hyperparameters.
tolerance.g	The tolerance for the absolute change in the gradient of the log posterior in the optimisation of the hyperparameters.
tolerance.x	The tolerance for the change in the hyperparameters (root-mean-square) in the optimisation of the hyperparameters.
restart	To improve the optimisation, the optimiser is restarted at the found optimum 'restart' number of times.
optimiser	The optimiser to use; one of 'gsl', 'domin' or 'default'.
verbose	A boolean variable; run in verbose mode? (Default FALSE)
reordering	Type of reordering to use. (EXPERT OPTION; one of "AUTO", "DEFAULT", "IDENTITY", "REVERSEIDENTITY", "BAND", "METIS", "GENMMD", "AMD", "MD", "MMD", "AMDBAR", "AMDC", "AMDBARC", or the output from inla.qreordering.)
cpo.diff	Threshold to define when the cpo-calculations are inaccurate. (EXPERT OPTION.)
npoints	Number of points to use in the 'stratey=laplace' approximation
cutoff	The cutoff used in the 'stratey=laplace' approximation. (Smaller value is more accurate and more slow.)
adapt.hessian.mode	A boolean variable; should optimisation be continued if the Hessian estimate is void? (Default TRUE)
adapt.hessian.max.trials	Number of steps in the adaptive Hessian optimisation
adapt.hessian.scale	The scaling of the 'h' after each trial.
huge	A boolean variable; if TRUE then try to do some of the internal parallisations differently. Hopefully this will be of benefite for 'HUGE' models. (Default FALSE.) [THIS OPTION IS OBSOLETE AND NOT USED!]
step.len	The step-length used to compute numerical derivaties of the log-likelihood
stencil	Number of points in the stencil used to compute the numerical derivaties of the log-likelihood (3, 5 or 7).
lincomb.derived.only	A boolean variable: if TRUE the only compute the marginals for the derived linear combinations and if FALSE, the and also the linear combinations to the graph (Default TRUE)
lincomb.derived.correlation.matrix	A boolean variable: if TRUE compute also the correlations for the derived linear combinations, if FALSE do not (Default FALSE)
diagonal	Expert use only! Add a this value on the diagonal of the joint precision matrix.
numint.maxfeval	Maximum number of function evaluations in the the numerical integration for the hyperparameters. (Default 10000.)
numint.relerr	Relative error requirement in the the numerical integration for the hyperparameters. (Default 1e-5)
numint.abserr	Absolute error requirement in the the numerical integration for the hyperparameters. (Default 1e-6)
cmin	The minimum value for the negative Hessian from the likelihood. Increasing this value will stabalise the optimisation. (Default 0.0)
step.factor	The step factor in the Newton-Raphson algorithm saying how large step to take (Default 1.0)
global.node.factor	The factor which defines the degree required (how many neighbors), as a fraction of n-1, that is required to be classified as a global node and numbered last (whatever the reordering routine says). Here, n, is the size of the graph. (Disabled if larger than 1.)
global.node.degree	The degree required (number of neighbors) to be classified as a global node and numbered last (whatever the reordering routine says).
stupid.search	Enable or disable the stupid-search-algorithm, if the Hessian calculations reveals that the mode is not found. (Default TRUE.)
stupid.search.max.iter	Maximum number of iterations allowed for the stupid-search-algorithm.
stupid.search.factor	Factor (>=1) to increase the step-length with after each new interation.
correct	Add correction for the Laplace approximation.
correct.factor	Factor used in adjusting the correction factor (default=10) if correct=TRUE
correct.strategy	The strategy used to compute the correction; one of 'simplified.laplace' (default) or 'laplace'
correct.verbose	Be verbose when computing the correction?

Value

The function control.inla is used to TAB-complete arguments and returns a list of given arguments. The function inla.set.control.inla.default returns a list with all the default values of all parameters within this control statement.

See Also

control.update, control.lincomb, control.group, control.mix, control.link, control.expert, control.compute, control.family, control.fixed, control.inla, control.predictor, control.results, control.mode, control.hazard, inla

andrewzm/INLA documentation built on May 10, 2019, 11:12 a.m.