bootcor_restr {highriskzone} | R Documentation |
Bootstrap correction to obtain desired failure probability
Description
Simulation-based iterative procedure to correct for possible bias with respect to the failure probability alpha
Usage
bootcor_restr(
ppdata,
cutoff,
numit = 100,
tol = 0.001,
nxprob = 0.1,
hole = NULL,
obsprobimage = NULL,
intens = NULL,
covmatrix = NULL,
simulate = "intens",
radiusClust = NULL,
clustering = 5,
verbose = TRUE
)
Arguments
ppdata |
Observed spatial point process of class ppp. |
cutoff |
Desired failure probability alpha, which is the probability of having unobserved events outside the high-risk zone. |
numit |
Number of iterations to perform (per tested value for cutoff). Default value is 1000. |
tol |
Tolerance: acceptable difference between the desired failure probability and the fraction of high-risk zones not covering all events. Default value is 0.02. |
nxprob |
Probability of having unobserved events. Default value is 0.1. |
hole |
(optional) an object of class |
obsprobimage |
(optional) an object of class |
intens |
(optional) estimated intensity of the observed process (object of class "im",
see |
covmatrix |
(optional) Covariance matrix of the kernel of a normal distribution, only meaningful if no intensity is given. If not given, it will be estimated. |
simulate |
The type of simulation, can be one of |
radiusClust |
(optional) radius of the circles around the parent points in which the cluster
points are located. Only used for |
clustering |
a value >= 1 which describes the amount of clustering; the
adjusted estimated intensity of the observed pattern is divided by
this value; it also is the parameter of the Poisson distribution
for the number of points per cluster. Only used for |
verbose |
logical. Should information on tested values/progress be printed? |
Details
For a desired failure probability alpha, the corresponding parameter which is to use
when determining a high-risk zone is found in an iterative procedure. The simulation procedure
is the same as in eval_method
. In every iteration,
the number of high-risk zones with at least one unobserved event located outside is
compared with the desired failure probability. If necessary, the value of cutoff
is
increased or decreased. The final value alphastar
can than be used in
det_hrz
.
The function offers the possibility to take into account so-called restriction areas. This is relevant in
situations where the observed point pattern ppdata
is incomplete. If it is known that no observations
can be made in a certain area (for example because of water expanses),
this can be accounted for by integrating a hole in the observation window.
The shape and location of the hole is given by hole
. Holes are
part of the resulting high-risk zone.
Another approach consists in weighting the observed events with their reciprocal observation probability when
estimating the intensity. To do so, the observation probability can be specified by using
obsprobsimage
(an image of the observation probability). Note that the
observation probability may vary in space.
For further information, see Mahling (2013), Appendix A (References).
If there are no restriction areas in the observation window, bootcor
can be used instead.
Value
An object of class bootcorr, which consists of a list of the final value for alpha (alphastar
)
and a data.frame course
containing information on the simulation course, e.g. the tested values.
References
Monia Mahling, Michael H?hle & Helmut K?chenhoff (2013), Determining high-risk zones for unexploded World War II bombs by using point process methodology. Journal of the Royal Statistical Society, Series C 62(2), 181-199.
Monia Mahling (2013), Determining high-risk zones by using spatial point process methodology. Ph.D. thesis, Cuvillier Verlag G?ttingen, available online: http://edoc.ub.uni-muenchen.de/15886/ Chapter 6 and Appendix A
See Also
Examples
data(craterA)
set.seed(4321)
# define restriction area
restrwin <- spatstat.geom::owin(xrange = craterA$window$xrange,
yrange = craterA$window$yrange,
poly = list(x = c(1500, 1500, 2000, 2000),
y = c(2000, 1500, 1500, 2000)))
# create image of observation probability (30% inside restriction area)
wim <- spatstat.geom::as.im(craterA$window, value = 1)
rim <- spatstat.geom::as.im(restrwin, xy = list(x = wim$xcol, y = wim$yrow))
rim$v[is.na(rim$v)] <- 0
oim1 <- spatstat.geom::eval.im(wim - 0.7 * rim)
## Not run:
# perform bootstrap correction
bc1 <- bootcor_restr(ppdata=craterA, cutoff=0.4, numit=100, tol=0.02, obsprobimage=oim1, nxprob=0.1)
bc1
summary(bc1)
plot(bc1)
# determine high-risk zone by weighting the observations
hrzi1 <- det_hrz_restr(ppdata=craterA, type = "intens", criterion = "indirect",
cutoff = bc1$alphastar, hole=NULL, obsprobs=NULL, obsprobimage=oim1, nxprob = 0.1)
# perform bootstrap correction
set.seed(4321)
bc2 <- bootcor_restr(ppdata=craterA, cutoff=0.4, numit=100, tol=0.02, hole=restrwin, nxprob=0.1)
bc2
summary(bc2)
plot(bc2)
# determine high-risk zone by accounting for a hole
hrzi2 <- det_hrz_restr(ppdata=craterA, type = "intens", criterion = "indirect",
cutoff = bc2$alphastar, hole=restrwin, obsprobs=NULL, obsprobimage=NULL, nxprob = 0.1)
## End(Not run)