analyzeGroup: Analysis for a single group of bullet holes

View source: R/analyzeGroup.R

analyzeGroupR Documentation

Analysis for a single group of bullet holes

Description

Performs a comprehensive numerical and graphical analysis of a single group of bullet holes.

Usage

analyzeGroup(DF, xyTopLeft = TRUE, center = FALSE,
             dstTarget, conversion, bandW = 0.5,
             CEPtype = 'CorrNormal', bootCI = 'none')

Arguments

DF

a data frame containing (at least) either the variables 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.

xyTopLeft

logical: is the origin of the absolute coordinate system in the top-left corner? See details.

center

logical: center groups to mean (0,0) first? If variable series does not exist, all shots are treated as belonging to the same group.

dstTarget

a numerical value giving the distance to the target - used in MOA calculation. Acts as override if variable distance is already included in DF. See getMOA.

conversion

how to convert the measurement unit for distance to target to that of the (x,y)-coordinates in MOA calculation. Acts as override if variables dist.unit and point.unit are already included in DF. Example 'm2cm'. See getMOA.

bandW

for argument bandwith of smoothScatter.

CEPtype

string vector indicating which CEP estimate to report in getCEP.

bootCI

a character vector to select which bootstrap confidence interval type to report. Possible types are 'none' (no bootstrap CI), 'norm', 'basic', 'perc', 'bca'. See boot.ci.

Details

By default, OnTarget PC/TDS' 'Export Point Data' places the origin of the absolute coordinate system in the top-left corner. In OnTarget TDS, this setting can be changed by checking the box 'Tools -> Options -> Options tab -> Data Export -> Invert Y-Axis on Export'. In that case, use 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:

  • a combined plot for multivariate outlier identification as produced by aq.plot

  • a scatterplot of the (x,y)-coordinates together with group center, circle with average distance to center, 50%-confidence ellipse - the latter also based on a robust estimate for the covariance matrix

  • a scatterplot of the (x,y)-coordinates together with the minimum bounding box, minimum enclosing circle, and maximum group spread

  • a chi-square Q-Q-plot for eyeballing multivariate normality as produced by chisq.plot, including a reference line with intercept 0 and slope 1

  • a heatmap of a 2D-kernel density estimate for the (x,y)-coordinates as produced by smoothScatter together with group center and error ellipse based on a robust estimate for the covariance matrix

  • a Q-Q-plot of x-coordinates for eyeballing normality

  • a Q-Q-plot of y-coordinates for eyeballing normality

  • a histogram of x-coordinates including a fitted normal distribution as well as a non-parametric kernel density estimate

  • a histogram of y-coordinates including a fitted normal distribution as well as a non-parametric kernel density estimate

  • a histogram of distances to group center including a fitted Rayleigh distribution as well as a non-parametric kernel density estimate

Value

A list with the results from the numerical analyses and statistical tests.

corXY

correlation matrix of (x,y)-coordinates.

corXYrob

robust estimate of correlation matrix of (x,y)-coordinates.

Outliers

a vector of row indices for observations identified as outliers.

ShapiroX

Shapiro-Wilk-Test result for normality of x-coordinates.

ShapiroY

Shapiro-Wilk-Test result for normality of y-coordinates.

multNorm

E-statistic-Test result for multivariate normality of (x,y)-coordinates.

sdXY

standard deviations of x- and y-coordinates (in original measurement units, MOA, SMOA, milliradian).

sdXci

parametric and bootstrap confidence intervals for the standard deviation of x-coordinates (in original measurement units, MOA, SMOA, milliradian).

sdYci

parametric and bootstrap confidence intervals for the standard deviation of y-coordinates (in original measurement units, MOA, SMOA, milliradian).

sdXYrob

robust standard deviations of x- and y-coordinates (in original measurement units, MOA, SMOA, milliradian).

covXY

covariance matrix of (x,y)-coordinates.

covXYrob

robust estimate of covariance matrix of (x,y)-coordinates.

distToCtr

mean and median distance from points to their center as well as estimated Rayleigh parameters sigma (precision), radial standard deviation RSD, and mean radius MR (in original measurement units, MOA, SMOA, milliradian).

sigmaCI

95%-parametric and bootstrap confidence intervals for sigma (in original measurement units, MOA, SMOA, milliradian).

RSDci

95%-parametric and bootstrap confidence intervals for radial standard deviation RSD (in original measurement units, MOA, SMOA, milliradian).

MRci

95%-parametric and bootstrap confidence intervals for mean radius MR (in original measurement units, MOA, SMOA, milliradian).

maxPairDist

maximum pairwise distance between points (center-to-center, = maximum spread, in original measurement units, MOA, SMOA, milliradian).

groupRect

width and height of bounding box with diagonal and figure of merit FoM (average side length, in original measurement units, MOA, SMOA, milliradian).

groupRectMin

width and height of minimum-area bounding box with diagonal and figure of merit FoM (average side length, in original measurement units, MOA, SMOA, milliradian).

minCircleRad

radius for the minimum enclosing circle (in original measurement units, MOA, SMOA, milliradian).

confEll

length of semi-major and semi-minor axis of the 50%-confidence ellipse (in original measurement units, MOA, SMOA, milliradian).

confEllRob

length of semi-major and semi-minor axis of the 50%-confidence ellipse based on a robust estimate for the covariance matrix (in original measurement units, MOA, SMOA, milliradian).

confEllShape

aspect ratio and flattening of the 50%-confidence ellipse.

confEllShapeRob

aspect ratio and flattening of the 50%-confidence ellipse based on a robust estimate for the covariance matrix.

CEP

estimate(s) for the 50%-circular error probable (CEP, in original measurement units, MOA, SMOA, milliradian).

ctr

(x,y)-offset of group center relative to point of aim.

ctrXci

95%-parametric and bootstrap confidence intervals for center x-coordinate.

ctrYci

95%-parametric and bootstrap confidence intervals for center y-coordinate.

ctrRob

robust estimate of group center offset relative to point of aim (MCD algorithm).

distPOA

distance from group center to point of aim (in original measurement units, MOA, SMOA, milliradian).

distPOArob

distance from robust estimate of group center to point of aim (in original measurement units, MOA, SMOA, milliradian).

Hotelling

Hotelling's T^2-Test result from testing if group center equals point of aim.

See Also

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

Examples

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

dwoll/shotGroups documentation built on Feb. 16, 2024, 2:21 p.m.