Description Usage Arguments Details Value See Also Examples
Evaluates the performance of the three methods:
Method of fixed radius
Quantilebased method
Intensitybased method
For further details on the methods, see det_hrz
or the paper of Mahling et al. (2013)(References).
There are three ways to simulate data for the evaluation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 
ppdata 
Observed spatial point process of class ppp. 
type 
Method to use, can be one of 
criterion 
criterion to limit the highrisk zone, can be one of

cutoff 
Value of criterion (area, radius, quantile, alpha or threshold). Depending on criterion and type: If criterion = "direct" and type = "intens", cutoff is the maximum intensity of unexploded bombs outside the risk zone. If type = "dist" instead, cutoff is the radius of the circle around each exploded bomb. "If criterion = "indirect", cutoff is the quantile for the quantilebased method and the failure probability alpha for the intensitybase method. If criterion = "area", cutoff is the area the highrisk zone should have. 
numit 
Number of iterations 
nxprob 
Probability of having unobserved events. Default value is 0.1. 
distancemap 
(optional) distance map: distance of every pixel to the nearest observation
of the point pattern; only needed for 
intens 
(optional) estimated intensity of the observed process (object of class "im"),
only needed for type="intens". If not given,
it will be estimated using 
covmatrix 
(optional) Covariance matrix of the kernel of a normal distribution, only needed for

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 is also the parameter of the Poisson distribution
for the number of points per cluster. Only used for 
pbar 
logical. Should progress bar be printed? 
The three simulation types are:
Here a given data set is used. The data set is thinned as explained below. Note that this method is very different from the others, since it is using the real data.
Here, an inhomogeneous Poisson process is simulated and then that data is thinned.
Here a NeymanScott process is simulated (see sim_nsppp
, rNeymanScott
)
and this data is then also thinned.
Thinning:
Let X be the spatial point process, which is the location of all events and let Y
be a subset of X describing the observed process. The process of unobserved events
then is Z = X \ Y , meaning that Z and Y are disjoint and together
forming X.
Since Z is not known, in this function an observed or simulated spatial point pattern
ppdata
is taken as the full pattern (which we denote by X') comprising the
observed events Y' as well as the unobserved Z'.
Each event in X' is assigned to one of the two processes Y' or
Z' by drawing independent Bernoulli random numbers.
The resulting process of observed events Y' is used to determine the highrisk zone.
Knowing now the unobserved process, it can be seen how many events are outside and inside the
highrisk zone.
type
and criterion
may be vectors in this function.
A data.frame
with variables
Iteration 
Iterationstep of the result 
Type, Criterion, Cutoff, nxprob 
see arguments 
threshold 
determined threshold. If criterion="area", it is either the distance (if type="dist") or the threshold c (for type="intens"). If criterion="indirect", it is either the quantile of the nearestneighbour distance which is used as radius (if type="dist") or the threshold c (for type="intens"). If criterion="direct", it equals the cutoff for both types. 
calccutoff 
determined cutoffvalue. For type="dist" and criterion="area", this is the quantile of the nearestneighbour distance. For type="intens" and criterion="area", it is the failure probability alpha. For all other criterions it is NA. 
covmatrix11, covmatrix12, covmatrix21, covmatrix22 
values in the covariance matrix. covmatrix11 and covmatrix22 are the diagonal elements (variances). 
numbermiss 
number of unobserved points outside the highrisk zone 
numberunobserved 
number of observations in the unobserved point pattern Z' 
missingfrac 
fraction of unobserved events outside the highrisk zone (numbermiss/numberunobserved) 
arearegion 
area of the highrisk zone 
numberobs 
number of observations in the observed point pattern Y' 
det_hrz
, rNeymanScott
, thin
, sim_nsppp
, sim_intens
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  ## Not run:
data(craterB)
# the input values are mainly the same as in det_hrz, so for more example ideas,
# see the documentation of det_hrz.
evalm < eval_method(craterB, type = c("dist", "intens"), criterion = c("area", "area"),
cutoff = c(1500000, 1500000), nxprob = 0.1, numit = 10,
simulate = "clintens", radiusClust = 300,
clustering = 15, pbar = FALSE)
evalm_d < subset(evalm, evalm$Type == "dist")
evalm_i < subset(evalm, evalm$Type == "intens")
# pout: fraction of highrisk zones that leave at least one unobserved event uncovered
# pmiss: Mean fraction of unobserved events outside the highrisk zone
data.frame(pmiss_d = mean(evalm_d$missingfrac),
pmiss_i = mean(evalm_i$missingfrac),
pout_d = ( sum(evalm_d$numbermiss > 0) / nrow(evalm_d) ),
pout_i = ( sum(evalm_i$numbermiss > 0) / nrow(evalm_i) ))
## End(Not run)

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