Internal wrapper to INLA and are called from fitAbn.bayes and buildScoreCache.bayes.
calc.node.inla.glmm(
child.loc = NULL,
dag.m.loc = NULL,
data.df.loc = NULL,
data.dists.loc = NULL,
ntrials.loc = NULL,
exposure.loc = NULL,
compute.fixed.loc = NULL,
mean.intercept.loc = NULL,
prec.intercept.loc = NULL,
mean.loc = NULL,
prec.loc = NULL,
loggam.shape.loc = NULL,
loggam.inv.scale.loc = NULL,
verbose.loc = FALSE,
nthreads = NULL
)If INLA failed, FALSE or an error is returned. Otherwise, the direct output from INLA is returned.
index of current child node.
dag as matrix.
data df,
list of distributions.
rep(1,dim(data.df)[1]).
rep(1,dim(data.df)[1]).
TRUE.
the prior mean for all the Gaussian additive terms for each node. INLA argument control.fixed=list(mean.intercept=...) and control.fixed=list(mean=...).
the prior precision for all the Gaussian additive term for each node. INLA argument control.fixed=list(prec.intercept=...) and control.fixed=list(prec=...).
the prior mean for all the Gaussian additive terms for each node. INLA argument control.fixed=list(mean.intercept=...) and control.fixed=list(mean=...). Same as mean.intercept.loc.
the prior precision for all the Gaussian additive term for each node. INLA argument control.fixed=list(prec.intercept=...) and control.fixed=list(prec=...). Same as prec.intercept.loc.
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)))).
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)))).
FALSE to not print additional output.
number of threads to use for INLA. Default is fit.control[["ncores"]] or build.control[["ncores"]] which is the number of cores specified in control and defaults to 1.
Other Bayes:
buildScoreCache(),
calc.node.inla.glm(),
fitAbn(),
getmarginals()