Description Usage Arguments Details Value Author(s) Examples
View source: R/measurementsResultMap.R
It evaluates values at locations
plume-wise and summarises the result by calls to simulationsApply
.
1 2 | measurementsResult(simulations, locations, kinds,
fun_p = NA, fun_Rp = NA, fun_pl = NA, fun_Rpl = NA)
|
simulations |
|
locations |
indices of locations, i.e. rows of |
kinds |
index or name of the layer of |
fun_p |
|
fun_Rp |
|
fun_pl |
|
fun_Rpl |
|
It is a general cost function, after specifying some parameters via replaceDefault
with type = "costFun"
it can be used as costFun
in optimiseSD
. Examples are cost functions related to plume detection as given in measurementsResultFunctions
.
"cost"
: result of fun_Rp
(must be a single value in order to qualify as costFun
in optimiseSD
)
"costPlumes"
: result of fun_p
, a matrix where each row represents a plume.
Kristina B. Helle, kristina.helle@uni-muenster.de
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | demo(radioactivePlumes_addProperties)
# sensor locations
sampleLocations1 = sample.int(nLocations(radioactivePlumes), 10)
# modify 'measurementsResult' to cost function 'singleDetection'
singleDetection = replaceDefault(measurementsResult, newDefaults = list(
kinds = "detectable",
fun_p = function(x, nout = 1){
y = 1 - max(x)
if (length(x) == 0){
y = 1
}
return(y)
},
fun_Rp = function(x, weight = 1, nout = 1){
mean(x * weight$totalDose)/mean(weight$totalDose)
}),
type = "costFun.optimiseSD")[[1]]
# compute cost for sensors at 'sampleLocations1'
singleDetection1 = singleDetection(
simulations = radioactivePlumes,
locations = sampleLocations1)
# results
# global cost: fraction of non detected plumes, weighted by their total dose:
singleDetection1[["cost"]]
singleDetection1[["costPlumes"]] # for each plume if it is detected (0) or not (1)
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