| Title: | Algorithms for Matching Bullet Lands |
| Version: | 0.1 |
| License: | GPL-3 |
| Date: | 2017-04-25 |
| Description: | Analyze bullet lands using nonparametric methods. We provide a reading routine for x3p files (see http://www.openfmc.org for more information) and a host of analysis functions designed to assess the probability that two bullets were fired from the same gun barrel. |
| URL: | https://github.com/erichare/bulletr |
| BugReports: | https://github.com/erichare/bulletr/issues |
| Imports: | xml2, zoo, ggplot2, plyr, dplyr, reshape2, plotly, robustbase, smoother |
| Depends: | R (≥ 3.1) |
| RoxygenNote: | 6.0.1 |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2017-04-25 23:59:38 UTC; erichare |
| Author: | Eric Hare [aut, cre], Heike Hofmann [aut] |
| Maintainer: | Eric Hare <erichare@iastate.edu> |
| Repository: | CRAN |
| Date/Publication: | 2017-04-26 05:31:52 UTC |
Table of the number of consecutive matches
Description
Table of the number of consecutive matches
Usage
CMS(match)
Arguments
match |
is a Boolean vector of matches/non-matches |
Value
a table of the number of the CMS and their frequencies
Examples
x <- rbinom(100, size = 1, prob = 1/3)
CMS(x == 1) # expected value for longest match is 3
Fit a LOESS model with bootstrap samples
Description
Fit a LOESS model with bootstrap samples
Usage
boot_fit_loess(bullet, groove, B = 1000, alpha = 0.95)
Arguments
bullet |
Bullet as returned from fortify_x3p |
groove |
Groove as returned from get_grooves |
B |
number of Bootstrap samples |
alpha |
The significance level |
3d topological surface measurements for one land of a bullet from the Hamby study
Description
Some more info - not sure at the moment which bullet this is. Describe structure.
Usage
br411
Format
a list
Align two surface cross cuts according to maximal correlation
Description
The bullet with the first name serves as a reference, the second bullet is shifted.
Usage
bulletAlign(data, value = "l30")
Arguments
data |
data frame consisting of at least two surface crosscuts as given by function |
value |
string of the variable to match. Defaults to l30, the variable returned from function |
Value
list consisting of a) the maximal cross correlation, b) the lag resulting in the highest cross correlation, and c) same data frame as input, but y vectors are aligned for maximal correlation between the
Identifying a reliable cross section
Description
Should be changed: x should just indicate lower and upper limit. That is cleaner and should speed things up as well.
Usage
bulletCheckCrossCut(path, bullet = NULL, distance = 25, xlimits = c(50,
500), minccf = 0.9, span = 0.03)
Arguments
path |
path to an x3p file |
bullet |
If passed in, the actual bullet already loaded |
distance |
positive numeric value indicating the distance between cross sections to use for a comparison |
xlimits |
vector of values between which to check for cross sections in a stable region |
minccf |
minimal value of cross correlation to indicate a stable region |
span |
The span for the loess smooth function |
Identify the number of maximum CMS between two bullet lands
Description
Identify the number of maximum CMS between two bullet lands
Usage
bulletGetMaxCMS(lof1, lof2, column = "resid", span = 35)
Arguments
lof1 |
dataframe of smoothed first signature |
lof2 |
dataframe of smoothed second signature |
column |
The column which to smooth |
span |
positive number for the smoothfactor to use for assessing peaks. |
Value
list of matching parameters, data set of the identified striae, and the aligned data sets.
Identify the number of maximum CMS between two bullet lands
Description
Identify the number of maximum CMS between two bullet lands
Usage
bulletGetMaxCMS_nist(lof1, lof2, column = "resid", span = 35)
Arguments
lof1 |
dataframe of smoothed first signature |
lof2 |
dataframe of smoothed second signature |
column |
The column which to smooth |
span |
positive number for the smoothfactor to use for assessing peaks. |
Value
list of matching parameters, data set of the identified striae, and the aligned data sets.
Smooth the surface of a bullet
Description
Smooth the surface of a bullet
Usage
bulletSmooth(data, span = 0.03, limits = c(-5, 5))
Arguments
data |
data frame as returned by the function |
span |
width of the smoother, defaults to 0.03 |
limits |
vector of the form c(min, max). Results will be limited to be between these values. |
Value
data frame of the same form as the input extended by the vector l30 for the smooth.
Fit a loess curve to a bullet data frame
Description
First, the surface measurements of the bullet land is trimmed to be within left and right groove as specified by vector groove.
A loess regression is fit to the remaining surface measurements and residuals are calculated.
The most extreme 0.25
The result is called the signature of the bullet land.
Usage
fit_loess(bullet, groove, span = 0.75)
Arguments
bullet |
The bullet object as returned from fortify_x3p |
groove |
vector of two numeric values indicating the location of the left and right groove. |
span |
The span to use for the loess regression |
Value
a list of a data frame of the original bullet measurements extended by loess fit, residuals, and standard errors and two plots: a plot of the fit, and a plot of the bullet's land signature.
Convert a list of x3p file into a data frame
Description
x3p format consists of a list with header info and a 2d matrix of scan depths. fortify_x3p turn the matrix into a variable within a data frame, using the parameters of the header as necessary.
Usage
fortify_x3p(x3p)
Arguments
x3p |
a file in x3p format as return by function read_x3p |
Value
data frame with variables x, y, and value
Examples
data(br411)
br411_fort <- fortify_x3p(br411)
head(br411_fort)
Estimate center and radius
Description
Assuming the variables x and y are describing points located on a circle, the function uses a likelihood approach to estimate center and radius of the circle.
Usage
getCircle(x, y)
Arguments
x |
numeric vector of values |
y |
numeric vector of values |
Value
three dimensional vector of the circle center (x0, y0) and the radius
Estimate the twist in a bullet land
Description
Estimation of the twist in a barrel follows roughly the process described by Chu et al (2010). At the moment, twist is estimated from a single land - but the twist should be the same for the whole barrel. Therefore all lands of the same barrel should have the same twist. A note on timing: at the moment calculating the twist rate for a bullet land takes several minutes. XXX TODO XXX make the different methods a parameter. Also, accept other input than the path - if we start with the flattened bulletland we get results much faster.
Usage
getTwist(path, bullet = NULL, twistlimit = NULL, cutoff = 0.75)
Arguments
path |
to a file in x3p format |
bullet |
data in x3p format as returned by function read_x3p |
twistlimit |
Constraint the possible twist value |
cutoff |
Use this for the quantile cutoff |
Value
numeric value estimating the twist
Examples
## Not run:
# execution takes several minutes
load("data/b1.rda")
twist <- getTwist(path="barrel 1 bullet 1", bullet = b1, twistlimit=c(-2,0)*1.5625)
## End(Not run)
Deprecated function use get_crosscut
Description
Deprecated function use get_crosscut
Usage
get_bullet(path, x = 243.75)
Arguments
path |
The path to the x3p file |
x |
The crosscut value |
Read a crosscut from a 3d surface file
Description
Read a crosscut from a 3d surface file
Usage
get_crosscut(path = NULL, x = 243.75, bullet = NULL)
Arguments
path |
path to an x3p file. The path will only be considered, if bullet is not specified. |
x |
level of the crosscut to be taken. If this level does not exist, the crosscut with the closest level is returned. |
bullet |
alternative access to the surface measurements. |
Value
data frame
Find the grooves of a bullet land
Description
Find the grooves of a bullet land
Usage
get_grooves(bullet, smoothfactor = 15, adjust = 10, groove_cutoff = 400,
mean_left = NULL, mean_right = NULL, mean_window = 100)
Arguments
bullet |
data frame with topological data |
smoothfactor |
The smoothing window to use |
adjust |
positive number to adjust the grooves |
groove_cutoff |
The index at which a groove cannot exist past |
mean_left |
If provided, the location of the average left groove |
mean_right |
If provided, the location of the average right groove |
mean_window |
The window around the means to use |
Identify the location and the depth of peaks and heights at a crosscut
Description
Identify the location and the depth of peaks and heights at a crosscut
Usage
get_peaks(loessdata, column = "resid", smoothfactor = 35, striae = TRUE,
window = TRUE)
Arguments
loessdata |
export from rollapply |
column |
The column which should be smoothed |
smoothfactor |
set to default of 35. Smaller values will pick up on smaller changes in the crosscut. |
striae |
If TRUE, show the detected striae on the plot |
window |
If TRUE, show the window of the striae on the plot |
Value
list of several objects:
Identify the location and the depth of peaks and heights at a crosscut
Description
Identify the location and the depth of peaks and heights at a crosscut
Usage
get_peaks_nist(loessdata, column = "resid", smoothfactor = 35,
striae = TRUE, window = TRUE)
Arguments
loessdata |
export from rollapply |
column |
The column which should be smoothed |
smoothfactor |
set to default of 35. Smaller values will pick up on smaller changes in the crosscut. |
striae |
If TRUE, show the detected striae on the plot |
window |
If TRUE, show the window of the striae on the plot |
Value
list of several objects:
Number of maximum consecutively matching striae
Description
Number of maximum consecutively matching striae
Usage
maxCMS(match)
Arguments
match |
is a Boolean vector of matches/non-matches |
Value
an integer value of the maximum number of consecutive matches
Examples
x <- rbinom(100, size = 1, prob = 1/3)
CMS(x == 1) # expected value for longest match is 3
maxCMS(x==1)
Plot a bullet land using plotly
Description
Plot a bullet land using plotly
Usage
plot_3d_land(path, bullet = NULL)
Arguments
path |
The path to the x3p file |
bullet |
If not null, use this pre-loaded bullet |
Estimate predictions and residuals for a circle fit of x and y
Description
estimate a circle, find predictive values and resiudals. depending on specification, vertical (regular) residuals or orthogonal residuals are computed.
Usage
predCircle(x, y, resid.method = "response")
Arguments
x |
vector of numeric values |
y |
vector of numeric values |
resid.method |
character, one of "response" or "ortho"(gonal) |
Value
data frame with predictions and residuals
Estimate predictions and residuals for a smooth of x and y
Description
Fit a smooth line throught x and y, find predictive values and resiudals.
Usage
predSmooth(x, y)
Arguments
x |
vector of numeric values |
y |
vector of numeric values |
Value
data frame with predictions and residuals
Process x3p file
Description
x3p file of a 3d topological bullet surface is processed at surface crosscut x,
the bullet grooves in the crosscuts are identified and removed, and a loess smooth
is used (see ?loess for details) to remove the big structure.
Usage
processBullets(bullet, name = "", x = 100, grooves = NULL, span = 0.75,
window = 0, ...)
Arguments
bullet |
file as returned from read_x3p |
name |
name of the bullet |
x |
(vector) of surface crosscuts to process. |
grooves |
The grooves to use as a two element vector, if desired |
span |
The span for the loess fit |
window |
The mean window around the ideal crosscut |
... |
Additional arguments, passed to the get_grooves function |
Value
data frame
Examples
data(br411)
br411_processed <- processBullets(br411, name = "br411")
Read an x3p file as an R Data Frame
Description
Read an x3p file as an R Data Frame
Usage
read_x3p(path, profiley = TRUE)
Arguments
path |
The file path to the x3p file |
profiley |
If TRUE, rotate the matrix (if necessary) to ensure a profile is taken across y |
Examples
## Not run:
br411 <- read_x3p("Br4 Bullet 4-1.x3p")
## End(Not run)
Sample every X element of a data frame
Description
Sample every X element of a data frame in x and y direction
Usage
sample_x3p(dframe, byxy = c(2, 2))
Arguments
dframe |
data frame with x and y variable |
byxy |
(vector) of numeric value indicating the sapling resolution. If a single number, the same resolution is used for x and y. |
Value
subset of the input variable
Examples
data(br411)
br411_fort <- fortify_x3p(br411)
br411_sample <- sample_x3p(br411_fort, byxy = c(4, 4))
head(br411_sample)
Predict smooth from a fit
Description
Predict smooth from a fit
Usage
smoothloess(x, y, span, sub = 2)
Arguments
x |
X values to use |
y |
Y values to use |
span |
The span of the loess fit |
sub |
Subsample factor |
Match striation marks across two cross sections based on previously identified peaks and valleys
Description
Match striation marks across two cross sections based on previously identified peaks and valleys
Usage
striation_identify(lines1, lines2)
Arguments
lines1 |
data frame as returned from get_peaks function. data frames are expected to have the following variables: xmin, xmax, group, type, bullet, heights |
lines2 |
data frame as returned from get_peaks function. data frames are expected to have the following variables: xmin, xmax, group, type, bullet, heights |
Value
data frame of the same form as lines1 and lines2, but consisting of an additional variable of whether the striation marks are matches
Convert a data frame into an x3p file
Description
Convert a data frame into an x3p file
Usage
unfortify_x3p(df)
Arguments
df |
A data frame produced by fortify_x3p |
Value
An x3p object
Examples
data(br411)
br411_fort <- fortify_x3p(br411)
br411_unfort <- unfortify_x3p(br411_fort)
identical(br411_unfort, br411)