These functions take an SLik
object (as produced by MSL
) and samples its parameter space in (hopefully) clever ways, not yet well documented. rparam
calls sample_volume
to define points targeting the likelihood maximum and the bounds of confidence intervals, with n
for these different targets dependent on the mean square error of prediction of likelihood at the maximum and at CI bounds.
rparam(object, n= 1, useEI = list(max=TRUE,profileCI=TRUE,rawCI=FALSE),
useCI = TRUE, verbose = interactive(), tryn=30*n,
level = 0.95, CIweight=Infusion.getOption("CIweight"))sample_volume(object, n = 6, useEI, vertices=NULL,
dlr = NULL, verbose = interactive(),
fixed = NULL, tryn= 30*n)
a data frame of parameter points. Only parameters variable in the SLik
object are considered.
an SLik
or SLik_j
object
The number of parameter points to be produced
List of booleans, each determining whether to use an “expected improvement” (EI) criterion (e.g. Bingham et al., 2014) to select candidate parameter points to better ascertain a particular focal point. The elements max
, profileCI
and rawCI
determine this for three types of focal points, respectively the MSL estimate, profile CI bounds, and full-dimensional bounds. When EI is used, n
points with best EI are selected among tryn
points randomly generated in some neighborhood of the focal point.
Points are sampled within a convex hull defined by vertices
. By default, these vertices are taken from object$fit$data
.
Whether to define points targeting the bounds of confidence intervals for the parameters. An expected improvement criterion is also used here.
If useCI
is TRUE
but confidence intervals are not available from the object
, such intervals are computed with coverage level
.
A (log)likelihood ratio threshold used to select points in the upper region of the likelihood surface. Default value is
given by Infusion.getOption("LRthreshold")
Whether to display some information about selection of points, or not
A list or named vector, of which each element is of the form <parameter name>=<value>
, defining a one-dimensional constraint in parameter space.
Points will be sampled in the intersection of the volume defined by the object
and of such constraint(s).
See useEI
argument.
For development purposes, not documented.
D. Bingham, P. Ranjan, and W.J. Welch (2014) Design of Computer Experiments for Optimization, Estimation of Function Contours, and Related Objectives, pp. 109-124 in Statistics in Action: A Canadian Outlook (J.F. Lawless, ed.). Chapman and Hall/CRC.
if (Infusion.getOption("example_maxtime")>10) {
data(densv)
summliksurf <- infer_surface(densv) ## infer a log-likelihood surface
sample_volume(summliksurf)
}
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