range2CEP | R Documentation |
Estimate the circular error probable (CEP) based on range statistics such as extreme spread, figure of merit, or the bounding box diagonal. This function assumes a circular bivariate normal shot distribution with 0 mean.
range2CEP(x, stat="ES", n, nGroups, CEPlevel=0.5, CIlevel=0.95,
collapse=TRUE, dstTarget, conversion)
x |
a numerical vector with values for extreme spread (ES), figure of merit (FoM), or the diagonal of the bounding box (D). |
stat |
a character vector with elements |
n |
integer between 2 and 100. Number of shots in each group. |
nGroups |
integer between 1 and 10. Number of groups when |
CEPlevel |
a numerical vector with the coverage values for the CEP. |
CIlevel |
confidence level (coverage probability) for the CEP confidence interval. If one of |
collapse |
logical: should the list with CIs be simplified if possible? |
dstTarget |
a numerical value giving the distance to the target - used in MOA calculation. See |
conversion |
how to convert the measurement unit for distance to target to that of the (x,y)-coordinates in MOA calculation. Example |
Based on the lookup table DFdistr
with results form a Monte Carlo simulation. The Rayleigh sigma parameter is estimated using range2sigma
, and then converted to CEP with qRayleigh
.
If package shiny
is installed, an interactive web app for this functionality can be run with runGUI("range")
.
A list with the calculated CEP values in one component, and the corresponding CIs in the other component.
CEP |
The calculated CEP values in the original measurement unit as well as in angular size measures. |
CEPCI |
The calculated CEP CIs in the original measurement unit as well as in angular size measures. |
Taylor, M. S., & Grubbs, F. E. (1975). Approximate Probability Distributions for the Extreme Spread (BRL-MR-2438). Aberdeen Proving Ground, MD: U.S. Ballistic Research Laboratory.
DFdistr
,
range2sigma
qRayleigh
getCEP
es <- getMaxPairDist(DFscar17)$d
fom <- getBoundingBox(DFscar17)$FoM
d <- getBoundingBox(DFscar17)$diag
range2CEP(c(es, fom, d), stat=c("ES", "FoM", "D"),
n=nrow(DFscar17), nGroups=1, CEPlevel=0.5, CIlevel=0.9)
# compare with Rayleigh CEP estimate from using
# (x,y)-coordinates of all shots
getCEP(DFscar17, CEPlevel=0.5, type="Rayleigh")
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