control.inla: Control variables in control.inla

In inbo/INLA: Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations

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, adaptive.max, 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.design, 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.step, tolerance.x, verbose)

Arguments

...	Possible arguments
strategy	Character The strategy to use for the approximations; one of 'gaussian', 'simplified.laplace' (default), 'laplace' or 'adaptive'
int.strategy	Character The integration strategy to use; one of 'auto' (default), 'ccd', 'grid', 'eb' (empirical bayes), 'user' or 'user.std'
int.design	Matrix Matrix of user-defined integration points and weights. Each row consists theta values and the integration weight. (EXPERIMENTAL!)
interpolator	Character 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	Logical If TRUE, then replace conditional modes in the Laplace approximation with conditional expectation (default TRUE)
linear.correction	Logical Default TRUE for the 'strategy = laplace' option.
h	Numerical The step-length for the gradient calculations for the hyperparameters. Default 0.01.
dz	Numerical The step-length in the standarised scale for the integration of the hyperparameters. Default 0.75.
diff.logdens	Numerical The difference of the log.density for the hyperpameters to stop numerical integration using int.strategy='grid'. Default 6.
print.joint.hyper	Logical If TRUE, the store also the joint distribution of the hyperparameters (without any costs). Default TRUE.
force.diagonal	Logical If TRUE, then force the Hessian to be diagonal. (Default FALSE)
skip.configurations	Logical Skip configurations if the values at the main axis are to small. (Default TRUE)
mode.known	Logical If TRUE then no optimisation is done. (Default FALSE.)
adjust.weights	Logical If TRUE then just more accurate integration weights. (Default TRUE.)
tolerance	Numerical 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	Numerical The tolerance for the absolute change in the log posterior in the optimisation of the hyperparameters.
tolerance.g	Numerical The tolerance for the absolute change in the gradient of the log posterior in the optimisation of the hyperparameters.
tolerance.x	Numerical The tolerance for the change in the hyperparameters (root-mean-square) in the optimisation of the hyperparameters.
tolerance.step	Numerical The tolerance for the change in root-mean_squre in the inner Newton-like optimisation of the latent field.
restart	Numerical To improve the optimisation, the optimiser is restarted at the found optimum 'restart' number of times.
optimiser	Character The optimiser to use; one of 'gsl' or 'default'.
verbose	Logical Run in verbose mode? (Default FALSE)
reordering	Character 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. Default is 'auto'.)
cpo.diff	Numerical Threshold to define when the cpo-calculations are inaccurate. (EXPERT OPTION.)
npoints	Numerical Number of points to use in the 'stratey=laplace' approximation (default 9)
cutoff	Numerical The cutoff used in the 'stratey=laplace' approximation. (Smaller value is more accurate and more slow.) (default 1e-4)
adapt.hessian.mode	Logical Should optimisation be continued if the Hessian estimate is void? (Default TRUE)
adapt.hessian.max.trials	Numerical Number of steps in the adaptive Hessian optimisation
adapt.hessian.scale	Numerical The scaling of the 'h' after each trial.
adaptive.max	Selecting strategy="adaptive" will chose the default strategy for all fixed effects and model components with length less or equal to adaptive.max, for others, the gaussian strategy will be applied.
huge	Logical 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	Numerical The step-length used to compute numerical derivaties of the log-likelihood
stencil	Numerical Number of points in the stencil used to compute the numerical derivaties of the log-likelihood (3, 5, 7 or 9). (default 5)
lincomb.derived.only	Logical 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	Logical If TRUE compute also the correlations for the derived linear combinations, if FALSE do not (Default FALSE)
diagonal	Numerical Expert use only! Add a this value on the diagonal of the joint precision matrix. (default 0.0)
numint.maxfeval	Numerical Maximum number of function evaluations in the the numerical integration for the hyperparameters. (Default 100000.)
numint.relerr	Numerical Relative error requirement in the the numerical integration for the hyperparameters. (Default 1e-5)
numint.abserr	Numerical Absolute error requirement in the the numerical integration for the hyperparameters. (Default 1e-6)
cmin	Numerical The minimum value for the negative Hessian from the likelihood. Increasing this value will stabalise the optimisation but can introduce bias in some estimates unless -Inf is used. (Default -Inf)
step.factor	Numerical The step factor in the Newton-Raphson algorithm saying how large step to take (Default 1.0)
global.node.factor	Numerical 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.) (default 2.0)
global.node.degree	Numerical The degree required (number of neighbors) to be classified as a global node and numbered last (whatever the reordering routine says). (default .Machine$integer.max)
stupid.search	Logical Enable or disable the stupid-search-algorithm, if the Hessian calculations reveals that the mode is not found. (Default TRUE.)
stupid.search.max.iter	Numerical Maximum number of iterations allowed for the stupid-search-algorithm. (default 1000)
stupid.search.factor	Numerical Factor (>=1) to increase the step-length with after each new interation. (default 1.05)
correct	Logical Add correction for the Laplace approximation. (default FALSE)
correct.factor	Numerical Factor used in adjusting the correction factor (default=10) if correct=TRUE
correct.strategy	Character The strategy used to compute the correction; one of 'simplified.laplace' (default) or 'laplace'
correct.verbose	Logical Be verbose when computing the correction? (default FALSE)

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.gev2.default, control.family, control.fixed, control.inla, control.predictor, control.results, control.mode, control.hazard, inla

inbo/INLA documentation built on Dec. 6, 2019, 9:51 a.m.