- 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".
- cluster.type
The type of cluster to be used, see ?parallel::makeCluster. abn then defaults to "PSOCK" on Windows and "FORK" on Unix-like systems. With "FORK" the child process are started with rscript_args = "--no-environ" to avoid loading the whole workspace into each child.
- 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).