analyzeGroup(DF, xyTopLeft = TRUE, conversion = 'm2cm', bandW = 0.5, CEPtype = 'CorrNormal', bootCI = c('basic', 'bca'))
Point.X
, Point.Y
or X
, Y
defining the bullet holes. Variables Distance
(distance to target), Aim.X
, Aim.Y
(point of aim) are useful - if they are missing, a warning is given and a default assumed.getMOA
.bandwith
of smoothScatter
.getCEP
.'none'
(no bootstrap CI), 'norm'
, 'basic'
, 'perc'
, 'bca'
. See boot.ci
.xyTopLeft=FALSE
. If groups appear to be upside-down, xyTopLeft
is the setting to change.Robust estimates for the group center and the covariance matrix of (x,y)-coordinates are from covMcd
using the MCD algorithm.
This function is a wrapper for groupShape
, groupLocation
, and groupSpread
.
If the data is missing information about the point of aim, (0,0) is assumed. If distance to target is missing, 100 is assumed.
The number of replicates for the reported bootstrap confidence intervals is at least 1499. If the BCa interval is reported, it is at least the number of points.
In addition to the numerical results listed below, this function produces the following diagrams:
aq.plot
chisq.plot
, including a reference line with intercept 0 and slope 1
smoothScatter
together with group center and error ellipse based on a robust estimate for the covariance matrix
groupShape
,
groupLocation
,
groupSpread
,
compareGroups
,
getDistToCtr
,
getMaxPairDist
,
getBoundingBox
,
getMinBBox
,
getMinCircle
,
getConfEll
,
getCEP
,
getRayParam
,
getMOA
,
smoothScatter
,
chisq.plot
,
aq.plot
,
pcout
,
qqnorm
,
hist
,
kernel
,
shapiro.test
,
mvnorm.etest
,
anova.mlm
,
boot
,
boot.ci
,
covMcd
data(DFinch)
# select combined data from only first 2 series
DF <- subset(DFinch, series %in% 1:2)
res <- analyzeGroup(DF, conversion='yd2in', bootCI='none')
names(res)
res$multNorm
res$corXY
res$ctrRob
res$ctrXci
res$ctrYci
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