fit.control: Control the iterations in 'fitAbn'
In abn: Modelling Multivariate Data with Additive Bayesian Networks

fit.control

R Documentation

Control the iterations in `fitAbn`

Description

Allow the user to set restrictions in the fitAbn for both the Bayesian and the MLE approach. Control function similar to build.control.

Usage

fit.control(
  method = "bayes",
  max.mode.error = 10,
  mean = 0,
  prec = 0.001,
  loggam.shape = 1,
  loggam.inv.scale = 5e-05,
  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,
  ncores = 1,
  max.irls = 100,
  tol = 1e-11,
  tolPwrss = 1e-07,
  check.rankX = "message+drop.cols",
  check.scaleX = "message+rescale",
  check.conv.grad = "message",
  check.conv.singular = "message",
  check.conv.hess = "message",
  xtol_abs = 1e-06,
  ftol_abs = 1e-06,
  trace.mblogit = FALSE,
  catcov.mblogit = "free",
  epsilon = 1e-06,
  seed = 9062019L
)

Arguments

`method`	a character that takes one of two values: "bayes" or "mle". Overrides `method` argument from `buildScoreCache`.
`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 also `fitAbn`.
`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", ...)`.
`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", ...)`.
`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", ...)`.
`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", ...)`.
`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.
`ncores`	The number of cores to parallelize to, see ‘Details’. If >0, the number of CPU cores to be used. -1 for all available -1 core. Only for `method="mle"`.
`max.irls`	total number of iterations for estimating network scores using an Iterative Reweighed Least Square algorithm. Is this DEPRECATED?
`tol`	real number giving the minimal tolerance expected to terminate the Iterative Reweighed Least Square algorithm to estimate network score. Passed to `irls_binomial_cpp_fast_br` and `irls_poisson_cpp_fast`.
`tolPwrss`	numeric scalar passed to `glmerControl` - the tolerance for declaring convergence in the penalized iteratively weighted residual sum-of-squares step. Similar to `tol`.
`check.rankX`	character passed to `lmerControl` and `glmerControl` - specifying if `rankMatrix(X)` should be compared with `ncol(X)` and if columns from the design matrix should possibly be dropped to ensure that it has full rank. Defaults to `message+drop.cols`.
`check.scaleX`	character passed to `lmerControl` and `glmerControl` - check for problematic scaling of columns of fixed-effect model matrix, e.g. parameters measured on very different scales. Defaults to `message+rescale`.
`check.conv.grad`	character passed to `lmerControl` and `glmerControl` - checking the gradient of the deviance function for convergence. Defaults to `message` but can be one of "ignore" - skip the test; "warning" - warn if test fails; "message" - print a message if test fails; "stop" - throw an error if test fails.
`check.conv.singular`	character passed to `lmerControl` and `glmerControl` - checking for a singular fit, i.e. one where some parameters are on the boundary of the feasible space (for example, random effects variances equal to 0 or correlations between random effects equal to +/- 1.0). Defaults to `message` but can be one of "ignore" - skip the test; "warning" - warn if test fails; "message" - print a message if test fails; "stop" - throw an error if test fails.
`check.conv.hess`	character passed to `lmerControl` and `glmerControl` - checking the Hessian of the deviance function for convergence. Defaults to `message` but can be one of "ignore" - skip the test; "warning" - warn if test fails; "message" - print a message if test fails; "stop" - throw an error if test fails.
`xtol_abs`	Defaults to 1e-6 stop on small change of parameter value. Only for `method='mle', group.var=...`. Default convergence tolerance for fitted `(g)lmer` models is reduced to the value provided here if default values did not fit. This value here is passed to the `optCtrl` argument of `(g)lmer` (see help of `lme4::convergence()`).
`ftol_abs`	Defaults to 1e-6 stop on small change in deviance. Similar to `xtol_abs`.
`trace.mblogit`	logical indicating if output should be produced for each iteration. Directly passed to `trace` argument in `mclogit.control`. Is independent of `verbose`.
`catcov.mblogit`	Defaults to "free" meaning that there are no restrictions on the covariances of random effects between the logit equations. Set to "diagonal" if random effects pertinent to different categories are uncorrelated or "single" if random effect variances pertinent to all categories are identical.
`epsilon`	Defaults to 1e-8. Positive convergence tolerance `\epsilon` that is directly passed to the `control` argument of `mclogit::mblogit` as `mclogit.control`. Only for `method='mle', group.var=...`.
`seed`	a non-negative integer which sets the seed in `set.seed(seed)`.

Details

Parallelization over all children is possible via the function foreach of the package doParallel. ncores=0 or ncores=1 use single threaded foreach. ncores=-1 uses all available cores but one.

Value

Named list according the provided arguments.

Examples

ctrlmle <- abn::fit.control(method = "mle",
                       max.irls = 100,
                       tol = 10^-11,
                       tolPwrss = 1e-7,
                       xtol_abs = 1e-6,
                       ftol_abs = 1e-6,
                       epsilon = 1e-6,
                       ncores = 2,
                       seed = 9062019L)
ctrlbayes <- abn::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,
                         ncores = 1,
                         seed = 9062019L)

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