| Title: | An Implementation of the 'Congruent Matching Cells' Method |
|---|---|
| Description: | An open-source implementation of the 'Congruent Matching Cells' method for cartridge case identification as proposed by Song (2013) <https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=911193> as well as an extension of the method proposed by Tong et al. (2015) <doi:10.6028/jres.120.008>. Provides a wide range of pre, inter, and post-processing options when working with cartridge case scan data and their associated comparisons. See the cmcR package website for more details and examples. |
| Authors: | Joe Zemmels [aut, cre], Heike Hofmann [aut], Susan VanderPlas [aut] |
| Maintainer: | Joe Zemmels <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.1.11 |
| Built: | 2025-01-10 05:18:29 UTC |
| Source: | https://github.com/csafe-isu/cmcr |
Plot Congruent Matching Cells results for a pair of cartridge cases.
cmcPlot( reference, target, cmcClassifs, type = "faceted", cmcCol = "originalMethod", corrCol = "pairwiseCompCor" )cmcPlot( reference, target, cmcClassifs, type = "faceted", cmcCol = "originalMethod", corrCol = "pairwiseCompCor" )
reference |
the scan that is partitioned into a grid of cells |
target |
the scan to which each reference cell is compared during the cell-based comparison procedure |
cmcClassifs |
a data frame containing columns cellHeightValues, alignedTargetCell, cellIndex, theta, and user-defined cmcCol & corrCol |
type |
the form of the returned plot object(s). Either "faceted," meaning the reference and target plot will be shown side-by-side or "list" meaning each element of the plot (referece, target, and legend) will be returned separately as elements of a list |
cmcCol |
name of column containing CMC classifications as returned by the decision_CMC function. Defaults to "originalMethod" |
corrCol |
name of column containing correlation values for each cell. Defaults to "pairwiseCompCor," but "fft_ccf" is a common alternative. |
Extract a matrix from the target region of the same dimension as the reference cell depending on the estimated translation calculated from comparison_fft_ccf
comparison_alignedTargetCell( cellHeightValues, regionHeightValues, target, theta, fft_ccf_df )comparison_alignedTargetCell( cellHeightValues, regionHeightValues, target, theta, fft_ccf_df )
cellHeightValues |
list/tibble column of x3p objects containing a reference scan's cells (as returned by comparison_cellDivision) |
regionHeightValues |
list/tibble column of x3p objects containing a target scan's regions (as returned by comparison_getTargetRegions) |
target |
the scan to which each cell in the partitioned scan was compared. |
theta |
the theta (rotation) value associated with each cellHeightValues, regionHeightValues pairing |
fft_ccf_df |
data frame/tibble column containing the data frame of (x,y) and CCF values returned by comparison_fft_ccf |
a list of x3p objects containing surface matrices extracted from regionHeightValues of the same dimension as the x3p objects in cellHeightValues
Performs all steps in the cell-based comparison procedure.
comparison_allTogether( reference, target, theta = 0, numCells = c(8, 8), maxMissingProp = 0.85, sideLengthMultiplier = 3, returnX3Ps = FALSE )comparison_allTogether( reference, target, theta = 0, numCells = c(8, 8), maxMissingProp = 0.85, sideLengthMultiplier = 3, returnX3Ps = FALSE )
reference |
an x3p object containing a breech face scan to be treated as the "reference scan" partitioned into a grid of cells |
target |
an x3p object containing a breech face scan to be treated as the "target scan" that the reference scan's cells are compared to |
theta |
degrees that the target scan is to be rotated prior extracting regions. |
numCells |
a vector of two numbers representing the number of cells along the row and column dimensions into which the x3p is partitioned |
maxMissingProp |
maximum proportion of missing values allowed for each cell/region. |
sideLengthMultiplier |
ratio between the target region and reference cell side lengths. For example, sideLengthMultiplier = 3 implies each region will be 9 times larger than its paired reference cell. |
returnX3Ps |
boolean to return the cellHeightValues and alignedTargetCells for each cell index. Note that setting this argument to TRUE significantly increases the size of the returned object. data(fadul1.1_processed,fadul1.2_processed) comparisonDF <- comparison_allTogether(reference = fadul1.1_processed, target = fadul1.2_processed) head(comparisonDF) |
a tibble object containing cell indices and the x, y, FFT-based CCF, and pairwise-complete correlation associated with the comparison between each cell and its associated target scan region (after rotating the target scan by theta degrees)
data(fadul1.1_processed,fadul1.2_processed) cellTibble <- comparison_allTogether(reference = fadul1.1_processed,target = fadul1.2_processed) head(cellTibble)data(fadul1.1_processed,fadul1.2_processed) cellTibble <- comparison_allTogether(reference = fadul1.1_processed,target = fadul1.2_processed) head(cellTibble)
Calculate the proportion of missing values in a breech face scan
comparison_calcPropMissing(heightValues)comparison_calcPropMissing(heightValues)
heightValues |
list/tibble column of x3p objects |
a vector of the same length as the input containing the proportion of missing values in each x3p object's breech face scan.
data(fadul1.1_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) %>% dplyr::mutate(cellPropMissing = comparison_calcPropMissing(heightValues = cellHeightValues)) head(cellTibble)data(fadul1.1_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) %>% dplyr::mutate(cellPropMissing = comparison_calcPropMissing(heightValues = cellHeightValues)) head(cellTibble)
Split a reference scan into a grid of cells
x3p |
an x3p object containing a breech face scan |
numCells |
a vector of two numbers representing the number of cells along the row and column dimensions into which the x3p is partitioned |
A tibble containing a prod(numCells) number of rows. Each row contains a single cell's index of the form (row #, col #) and an x3p object containing the breech face scan of that cell.
data(fadul1.1_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) head(cellTibble)data(fadul1.1_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) head(cellTibble)
Calculates correlation between a cell and a matrix of the same dimensions extracted from the cell's associated region.
comparison_cor( cellHeightValues, regionHeightValues, fft_ccf_df, use = "pairwise.complete.obs" )comparison_cor( cellHeightValues, regionHeightValues, fft_ccf_df, use = "pairwise.complete.obs" )
cellHeightValues |
list/tibble column of x3p objects containing a reference scan's cells (as returned by comparison_cellDivision) |
regionHeightValues |
list/tibble column of x3p objects containing a target scan's regions (as returned by comparison_getTargetRegions) |
fft_ccf_df |
data frame/tibble column containing the data frame of (x,y) and CCF values returned by comparison_fft_ccf |
use |
argument for stats::cor |
A vector of the same length as the input containing correlation values at the estimated alignment between each reference cell and its associated target region
data(fadul1.1_processed,fadul1.2_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) %>% dplyr::mutate(regionHeightValues = comparison_getTargetRegions(cellHeightValues = cellHeightValues, target = fadul1.2_processed)) %>% dplyr::mutate(cellPropMissing = comparison_calcPropMissing(heightValues = cellHeightValues), regionPropMissing = comparison_calcPropMissing(heightValues = regionHeightValues)) %>% dplyr::filter(cellPropMissing <= .85 & regionPropMissing <= .85) %>% dplyr::mutate(cellHeightValues = comparison_standardizeHeights(heightValues = cellHeightValues), regionHeightValues = comparison_standardizeHeights(heightValues = regionHeightValues)) %>% dplyr::mutate(cellHeightValues = comparison_replaceMissing(heightValues = cellHeightValues), regionHeightValues = comparison_replaceMissing(heightValues = regionHeightValues)) %>% dplyr::mutate(fft_ccf_df = comparison_fft_ccf(cellHeightValues, regionHeightValues)) %>% dplyr::mutate(pairwiseCompCor = comparison_cor(cellHeightValues, regionHeightValues, fft_ccf_df)) head(cellTibble)data(fadul1.1_processed,fadul1.2_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) %>% dplyr::mutate(regionHeightValues = comparison_getTargetRegions(cellHeightValues = cellHeightValues, target = fadul1.2_processed)) %>% dplyr::mutate(cellPropMissing = comparison_calcPropMissing(heightValues = cellHeightValues), regionPropMissing = comparison_calcPropMissing(heightValues = regionHeightValues)) %>% dplyr::filter(cellPropMissing <= .85 & regionPropMissing <= .85) %>% dplyr::mutate(cellHeightValues = comparison_standardizeHeights(heightValues = cellHeightValues), regionHeightValues = comparison_standardizeHeights(heightValues = regionHeightValues)) %>% dplyr::mutate(cellHeightValues = comparison_replaceMissing(heightValues = cellHeightValues), regionHeightValues = comparison_replaceMissing(heightValues = regionHeightValues)) %>% dplyr::mutate(fft_ccf_df = comparison_fft_ccf(cellHeightValues, regionHeightValues)) %>% dplyr::mutate(pairwiseCompCor = comparison_cor(cellHeightValues, regionHeightValues, fft_ccf_df)) head(cellTibble)
Estimate translation alignment between a cell/region pair based on the Cross-Correlation Theorem.
comparison_fft_ccf(cellHeightValues, regionHeightValues)comparison_fft_ccf(cellHeightValues, regionHeightValues)
cellHeightValues |
list/tibble column of x3p objects containing a reference scan's cells (as returned by comparison_cellDivision) |
regionHeightValues |
list/tibble column of x3p objects containing a target scan's regions (as returned by comparison_getTargetRegions) |
A list of the same length as the input containing data frames of the translation (x,y) values at which each reference cell is estimated to align in its associated target region and the CCF value at this alignment.
a data frame containing the translation (x,y) at which the CCF was maximized in aligning a target scan region to its associated reference scan cell.
The FFT is not defined for matrices containing missing values. The missing values in the cell and region need to be replaced before using this function. See the comparison_replaceMissing function to replace missing values after standardization.
https://mathworld.wolfram.com/Cross-CorrelationTheorem.html
data(fadul1.1_processed,fadul1.2_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) %>% dplyr::mutate(regionHeightValues = comparison_getTargetRegions(cellHeightValues = cellHeightValues, target = fadul1.2_processed)) %>% dplyr::mutate(cellPropMissing = comparison_calcPropMissing(heightValues = cellHeightValues), regionPropMissing = comparison_calcPropMissing(heightValues = regionHeightValues)) %>% dplyr::filter(cellPropMissing <= .85 & regionPropMissing <= .85) %>% dplyr::mutate(cellHeightValues = comparison_standardizeHeights(heightValues = cellHeightValues), regionHeightValues = comparison_standardizeHeights(heightValues = regionHeightValues)) %>% dplyr::mutate(cellHeightValues = comparison_replaceMissing(heightValues = cellHeightValues), regionHeightValues = comparison_replaceMissing(heightValues = regionHeightValues)) %>% dplyr::mutate(fft_ccf_df = comparison_fft_ccf(cellHeightValues, regionHeightValues)) cellTibble %>% tidyr::unnest(cols = fft_ccf_df) %>% head()data(fadul1.1_processed,fadul1.2_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) %>% dplyr::mutate(regionHeightValues = comparison_getTargetRegions(cellHeightValues = cellHeightValues, target = fadul1.2_processed)) %>% dplyr::mutate(cellPropMissing = comparison_calcPropMissing(heightValues = cellHeightValues), regionPropMissing = comparison_calcPropMissing(heightValues = regionHeightValues)) %>% dplyr::filter(cellPropMissing <= .85 & regionPropMissing <= .85) %>% dplyr::mutate(cellHeightValues = comparison_standardizeHeights(heightValues = cellHeightValues), regionHeightValues = comparison_standardizeHeights(heightValues = regionHeightValues)) %>% dplyr::mutate(cellHeightValues = comparison_replaceMissing(heightValues = cellHeightValues), regionHeightValues = comparison_replaceMissing(heightValues = regionHeightValues)) %>% dplyr::mutate(fft_ccf_df = comparison_fft_ccf(cellHeightValues, regionHeightValues)) cellTibble %>% tidyr::unnest(cols = fft_ccf_df) %>% head()
Extract regions from a target scan based on associated cells in reference scan
comparison_getTargetRegions( cellHeightValues, target, theta = 0, sideLengthMultiplier = 3, ... )comparison_getTargetRegions( cellHeightValues, target, theta = 0, sideLengthMultiplier = 3, ... )
cellHeightValues |
list/tibble column of x3p objects containing a reference scan's cells (as returned by comparison_cellDivision) |
target |
x3p object containing a breech face scan to be compared to the reference cell. |
theta |
degrees that the target scan is to be rotated prior extracting regions. |
sideLengthMultiplier |
ratio between the target region and reference cell side lengths. For example, sideLengthMultiplier = 3 implies each region will be 9 times larger than its paired reference cell. |
... |
internal usage |
A list of the same length as the input containing x3p objects from the target scan.
data(fadul1.1_processed,fadul1.2_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) %>% dplyr::mutate(regionHeightValues = comparison_getTargetRegions(cellHeightValues = cellHeightValues, target = fadul1.2_processed)) %>% dplyr::mutate(cellPropMissing = comparison_calcPropMissing(heightValues = cellHeightValues), regionPropMissing = comparison_calcPropMissing(heightValues = regionHeightValues)) %>% dplyr::filter(cellPropMissing <= .85 & regionPropMissing <= .85) head(cellTibble)data(fadul1.1_processed,fadul1.2_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) %>% dplyr::mutate(regionHeightValues = comparison_getTargetRegions(cellHeightValues = cellHeightValues, target = fadul1.2_processed)) %>% dplyr::mutate(cellPropMissing = comparison_calcPropMissing(heightValues = cellHeightValues), regionPropMissing = comparison_calcPropMissing(heightValues = regionHeightValues)) %>% dplyr::filter(cellPropMissing <= .85 & regionPropMissing <= .85) head(cellTibble)
Replace missing values in a scan
comparison_replaceMissing(heightValues, replacement = 0)comparison_replaceMissing(heightValues, replacement = 0)
heightValues |
list/tibble column of x3p objects |
replacement |
value to replace NAs |
A list of the same length as the input containing x3p objects for which NA values have been replaced.
data(fadul1.1_processed,fadul1.2_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) %>% dplyr::mutate(regionHeightValues = comparison_getTargetRegions(cellHeightValues = cellHeightValues, target = fadul1.2_processed)) %>% dplyr::mutate(cellPropMissing = comparison_calcPropMissing(heightValues = cellHeightValues), regionPropMissing = comparison_calcPropMissing(heightValues = regionHeightValues)) %>% dplyr::filter(cellPropMissing <= .85 & regionPropMissing <= .85) %>% dplyr::mutate(cellHeightValues = comparison_standardizeHeights(heightValues = cellHeightValues), regionHeightValues = comparison_standardizeHeights(heightValues = regionHeightValues)) %>% dplyr::mutate(cellHeightValues = comparison_replaceMissing(heightValues = cellHeightValues), regionHeightValues = comparison_replaceMissing(heightValues = regionHeightValues)) head(cellTibble)data(fadul1.1_processed,fadul1.2_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) %>% dplyr::mutate(regionHeightValues = comparison_getTargetRegions(cellHeightValues = cellHeightValues, target = fadul1.2_processed)) %>% dplyr::mutate(cellPropMissing = comparison_calcPropMissing(heightValues = cellHeightValues), regionPropMissing = comparison_calcPropMissing(heightValues = regionHeightValues)) %>% dplyr::filter(cellPropMissing <= .85 & regionPropMissing <= .85) %>% dplyr::mutate(cellHeightValues = comparison_standardizeHeights(heightValues = cellHeightValues), regionHeightValues = comparison_standardizeHeights(heightValues = regionHeightValues)) %>% dplyr::mutate(cellHeightValues = comparison_replaceMissing(heightValues = cellHeightValues), regionHeightValues = comparison_replaceMissing(heightValues = regionHeightValues)) head(cellTibble)
Standardize height values of a scan by centering/scaling by desired statistics and replacing missing values
comparison_standardizeHeights( heightValues, withRespectTo = "individualCell", centerBy = mean, scaleBy = sd )comparison_standardizeHeights( heightValues, withRespectTo = "individualCell", centerBy = mean, scaleBy = sd )
heightValues |
list/tibble column of x3p objects |
withRespectTo |
currently ignored |
centerBy |
statistic by which to center (i.e., subtract from) the height values |
scaleBy |
statistic by which to scale (i.e., divide) the height values |
A list of the same length as the input containing x3p objects with standardized surface matrices
this function adds information to the metainformation of the x3p scan it is given that is required for calculating, for example, the pairwise-complete correlation using the comparison_cor function.
data(fadul1.1_processed,fadul1.2_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) %>% dplyr::mutate(regionHeightValues = comparison_getTargetRegions(cellHeightValues = cellHeightValues, target = fadul1.2_processed)) %>% dplyr::mutate(cellPropMissing = comparison_calcPropMissing(heightValues = cellHeightValues), regionPropMissing = comparison_calcPropMissing(heightValues = regionHeightValues)) %>% dplyr::filter(cellPropMissing <= .85 & regionPropMissing <= .85) %>% dplyr::mutate(cellHeightValues = comparison_standardizeHeights(heightValues = cellHeightValues), regionHeightValues = comparison_standardizeHeights(heightValues = regionHeightValues)) head(cellTibble)data(fadul1.1_processed,fadul1.2_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = c(8,8)) %>% dplyr::mutate(regionHeightValues = comparison_getTargetRegions(cellHeightValues = cellHeightValues, target = fadul1.2_processed)) %>% dplyr::mutate(cellPropMissing = comparison_calcPropMissing(heightValues = cellHeightValues), regionPropMissing = comparison_calcPropMissing(heightValues = regionHeightValues)) %>% dplyr::filter(cellPropMissing <= .85 & regionPropMissing <= .85) %>% dplyr::mutate(cellHeightValues = comparison_standardizeHeights(heightValues = cellHeightValues), regionHeightValues = comparison_standardizeHeights(heightValues = regionHeightValues)) head(cellTibble)
Applies the decision rules of the original method of Song (2013) or the High CMC method of Tong et al. (2015)
decision_CMC( cellIndex, x, y, theta, corr, xThresh = 20, yThresh = xThresh, thetaThresh = 6, corrThresh = 0.5, tau = NULL )decision_CMC( cellIndex, x, y, theta, corr, xThresh = 20, yThresh = xThresh, thetaThresh = 6, corrThresh = 0.5, tau = NULL )
cellIndex |
vector/tibble column containing cell indices corresponding to a reference cell |
x |
vector/tibble column containing x horizontal translation values |
y |
vector/tibble column containing y vertical translation values |
theta |
vector/tibble column containing theta rotation values |
corr |
vector/tibble column containing correlation similarity scores between a reference cell and its associated target region |
xThresh |
used to classify particular x values "congruent" (conditional on a particular theta value) if they are within xThresh of the theta-specific median x value |
yThresh |
used to classify particular y values "congruent" (conditional on a particular theta value) if they are within yThresh of the theta-specific median y value |
thetaThresh |
(original method of Song (2013)) used to classify particular theta values "congruent" if they are within thetaThresh of the median theta value. (High CMC) defines how wide a High CMC mode is allowed to be in the CMC-theta distribution before it's considered too diffuse |
corrThresh |
to classify particular correlation values "congruent" (conditional on a particular theta value) if they are at least corrThresh |
tau |
(optional) parameter required to apply the High CMC method of Tong et al. (2015). If not given, then the decision rule of the original method of Song (2013) is applied. This number is subtracted from the maximum CMC count achieved in the CMC-theta distribution. Theta values with CMC counts above this value are considered to have "high" CMC counts. |
A vector of the same length as the input containing the CMC classification under one of the two decision rules.
https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=911193
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4730689/pdf/jres.120.008.pdf
## Not run: data(fadul1.1_processed,fadul1.2_processed) comparisonDF <- purrr::map_dfr(seq(-30,30,by = 3), ~ comparison_allTogether(fadul1.1_processed, fadul1.2_processed, theta = .)) comparisonDF <- comparisonDF %>% dplyr::mutate(originalMethodClassif = decision_CMC(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor), highCMCClassif = decision_CMC(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor, tau = 1)) comparisonDF %>% dplyr::filter(originalMethodClassif == "CMC" | highCMCClassif == "CMC") ## End(Not run)## Not run: data(fadul1.1_processed,fadul1.2_processed) comparisonDF <- purrr::map_dfr(seq(-30,30,by = 3), ~ comparison_allTogether(fadul1.1_processed, fadul1.2_processed, theta = .)) comparisonDF <- comparisonDF %>% dplyr::mutate(originalMethodClassif = decision_CMC(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor), highCMCClassif = decision_CMC(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor, tau = 1)) comparisonDF %>% dplyr::filter(originalMethodClassif == "CMC" | highCMCClassif == "CMC") ## End(Not run)
Combines CMC results from two comparison directions of a single cartridge case pair (i.e., where each cartridge case scan has been treated as both the reference and target scan). This function assumes that the CMC results are data frames withcolumns called "originalMethodClassif" and "highCMCClassif" containing CMCs identified under the original method of Song (2013) and the High CMC method of Tong et al. (2015) (see example).
decision_combineDirections( reference_v_target_CMCs, target_v_reference_CMCs, corColName = "pairwiseCompCor", missingThetaDecision = "fail", compareThetas = TRUE, thetaThresh = 6 )decision_combineDirections( reference_v_target_CMCs, target_v_reference_CMCs, corColName = "pairwiseCompCor", missingThetaDecision = "fail", compareThetas = TRUE, thetaThresh = 6 )
reference_v_target_CMCs |
CMCs for the comparison between the reference scan and the target scan. |
target_v_reference_CMCs |
(optional) CMCs for the comparison between the target scan and the reference scan. If this is missing, then only the original method CMCs will be plotted |
corColName |
name of correlation similarity score column used to identify the CMCs in the two comparison_*_df data frames (e.g., pairwiseCompCor) |
missingThetaDecision |
dictates how function should handle situations in which one direction passes the high CMC criterion while another direction does not. "dismiss": only counts the initial CMCs in failed direction and high CMCs in successful direction. "fail": only counts the initial CMCs in either direction and returns the minimum of these two numbers. |
compareThetas |
dictates if the consensus theta values determined under the initially proposed method should be compared to the consensus theta values determined under the High CMC method. In particular, determines for each direction whether the consensus theta values determined under the two methods are within theta_thresh of each other. It is often the case that non-matching cartridge cases, even if they pass the High CMC criterion, will have differing consensus theta values under the two methods. If this isn't taken into account, non-matches tend to be assigned a lot of false positive CMCs under the High CMC method. |
thetaThresh |
(original method of Song (2013)) used to classify particular theta values "congruent" if they are within thetaThresh of the median theta value. (High CMC) defines how wide a High CMC mode is allowed to be in the CMC-theta distribution before it's considered too diffuse. This is also used in this function to determine whether the estimated alignment theta values from the two comparison directions are "approximately" opposite (i.e., within thetaThresh of each other in absolute value), which they should be if the cartridge case pair is a known match. |
a list of 2 elements: (1) the CMCs identified under the original method of Song (2013) for both comparison directions since Song (2013) does not indicate whether/how results are combined and (2) the combined CMC results under the High CMC method.
## Not run: data(fadul1.1_processed,fadul1.2_processed) comparisonDF_1to2 <- purrr::map_dfr(seq(-30,30,by = 3), ~ comparison_allTogether(fadul1.1_processed, fadul1.2_processed, theta = .)) comparisonDF_2to1 <- purrr::map_dfr(seq(-30,30,by = 3), ~ comparison_allTogether(fadul1.2_processed, fadul1.1_processed, theta = .)) comparisonDF_1to2 <- comparisonDF_1to2 %>% dplyr::mutate(originalMethodClassif = decision_CMC(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor), highCMCClassif = decision_CMC(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor, tau = 1)) comparisonDF_2to1 <- comparisonDF_2to1 %>% dplyr::mutate(originalMethodClassif = decision_CMC(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor), highCMCClassif = decision_CMC(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor, tau = 1)) decision_combineDirections(comparisonDF_1to2,comparisonDF_2to1) ## End(Not run)## Not run: data(fadul1.1_processed,fadul1.2_processed) comparisonDF_1to2 <- purrr::map_dfr(seq(-30,30,by = 3), ~ comparison_allTogether(fadul1.1_processed, fadul1.2_processed, theta = .)) comparisonDF_2to1 <- purrr::map_dfr(seq(-30,30,by = 3), ~ comparison_allTogether(fadul1.2_processed, fadul1.1_processed, theta = .)) comparisonDF_1to2 <- comparisonDF_1to2 %>% dplyr::mutate(originalMethodClassif = decision_CMC(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor), highCMCClassif = decision_CMC(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor, tau = 1)) comparisonDF_2to1 <- comparisonDF_2to1 %>% dplyr::mutate(originalMethodClassif = decision_CMC(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor), highCMCClassif = decision_CMC(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor, tau = 1)) decision_combineDirections(comparisonDF_1to2,comparisonDF_2to1) ## End(Not run)
Compute CMC-theta distribution for a set of comparison features
decision_highCMC_cmcThetaDistrib( cellIndex, x, y, theta, corr, xThresh = 20, yThresh = xThresh, corrThresh = 0.5 )decision_highCMC_cmcThetaDistrib( cellIndex, x, y, theta, corr, xThresh = 20, yThresh = xThresh, corrThresh = 0.5 )
cellIndex |
vector/tibble column containing cell indices corresponding to a reference cell |
x |
vector/tibble column containing x horizontal translation values |
y |
vector/tibble column containing y vertical translation values |
theta |
vector/tibble column containing theta rotation values |
corr |
vector/tibble column containing correlation similarity scores between a reference cell and its associated target region |
xThresh |
used to classify particular x values "congruent" (conditional on a particular theta value) if they are within xThresh of the theta-specific median x value |
yThresh |
used to classify particular y values "congruent" (conditional on a particular theta value) if they are within yThresh of the theta-specific median y value |
corrThresh |
to classify particular correlation values "congruent" (conditional on a particular theta value) if they are at least corrThresh |
a vector of the same length as the input containing a "CMC Candidate" or "Non-CMC Candidate" classification based on whether the particular cellIndex has congruent x,y, and theta features.
This function is a helper internally called in the decision_CMC function. It is exported to be used as a diagnostic tool for the High CMC method
## Not run: data(fadul1.1_processed,fadul1.2_processed) comparisonDF <- purrr::map_dfr(seq(-30,30,by = 3), ~ comparison_allTogether(fadul1.1_processed, fadul1.2_processed, theta = .)) comparisonDF <- comparisonDF %>% dplyr::mutate(cmcThetaDistribClassif = decision_highCMC_cmcThetaDistrib(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor)) comparisonDF %>% dplyr::filter(cmcThetaDistribClassif == "CMC Candidate") %>% ggplot2::ggplot(ggplot2::aes(x = theta)) + ggplot2::geom_bar(stat = "count") ## End(Not run)## Not run: data(fadul1.1_processed,fadul1.2_processed) comparisonDF <- purrr::map_dfr(seq(-30,30,by = 3), ~ comparison_allTogether(fadul1.1_processed, fadul1.2_processed, theta = .)) comparisonDF <- comparisonDF %>% dplyr::mutate(cmcThetaDistribClassif = decision_highCMC_cmcThetaDistrib(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor)) comparisonDF %>% dplyr::filter(cmcThetaDistribClassif == "CMC Candidate") %>% ggplot2::ggplot(ggplot2::aes(x = theta)) + ggplot2::geom_bar(stat = "count") ## End(Not run)
Classify theta values in CMC-theta distribution as having "High" or "Low" CMC candidate counts
decision_highCMC_identifyHighCMCThetas(cmcThetaDistrib, tau = 1)decision_highCMC_identifyHighCMCThetas(cmcThetaDistrib, tau = 1)
cmcThetaDistrib |
output of the decision_highCMC_cmcThetaDistrib function |
tau |
constant used to define a "high" CMC count. This number is subtracted from the maximum CMC count achieved in the CMC-theta distribution. Theta values with CMC counts above this value are considered to have "high" CMC counts. |
A vector of the same length as the input containing "High" or "Low" classification based on whether the associated theta value has a High CMC Candidate count.
This function is a helper internally called in the decision_CMC function. It is exported to be used as a diagnostic tool for the High CMC method
## Not run: data(fadul1.1_processed,fadul1.2_processed) comparisonDF <- purrr::map_dfr(seq(-30,30,by = 3), ~ comparison_allTogether(fadul1.1_processed, fadul1.2_processed, theta = .)) highCMCthetas <- comparisonDF %>% dplyr::mutate(cmcThetaDistribClassif = decision_highCMC_cmcThetaDistrib(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor)) %>% decision_highCMC_identifyHighCMCThetas(tau = 1) highCMCthetas %>% dplyr::filter(cmcThetaDistribClassif == "CMC Candidate") %>% ggplot2::ggplot(ggplot2::aes(x = theta,fill = thetaCMCIdentif)) + ggplot2::geom_bar(stat = "count") ## End(Not run)## Not run: data(fadul1.1_processed,fadul1.2_processed) comparisonDF <- purrr::map_dfr(seq(-30,30,by = 3), ~ comparison_allTogether(fadul1.1_processed, fadul1.2_processed, theta = .)) highCMCthetas <- comparisonDF %>% dplyr::mutate(cmcThetaDistribClassif = decision_highCMC_cmcThetaDistrib(cellIndex = cellIndex, x = x, y = y, theta = theta, corr = pairwiseCompCor)) %>% decision_highCMC_identifyHighCMCThetas(tau = 1) highCMCthetas %>% dplyr::filter(cmcThetaDistribClassif == "CMC Candidate") %>% ggplot2::ggplot(ggplot2::aes(x = theta,fill = thetaCMCIdentif)) + ggplot2::geom_bar(stat = "count") ## End(Not run)
"Fadul 1-1" and "Fadul 1-2" cartridge cases from Fadul et al. (2011). The scans have been downsampled by a factor of 8 and processed using functions from the cmcR package.
fadul1.1_processed fadul1.2_processedfadul1.1_processed fadul1.2_processed
An x3p object containing a surface matrix and metainformation concerning the conditions under which the scan was taken
size and resolution of scan
spatially-ordered matrix of elements representing the height values of the processed cartridge case surface at particular locations
provides structure for storing surface data
information concerning the author of the scan and capturing device
provides link to surface measurements in binary format
An object of class x3p of length 5.
T. Fadul, G. Hernandez, S. Stoiloff, and G. Sneh. An Empirical Study to Improve the Scientific Foundation of Forensic Firearm and Tool Mark Identification Utilizing 10 Consecutively Manufactured Slides, 2011.
https://github.com/heike/x3ptools
Remove observations from the exterior of interior of a breech face scan
preProcess_crop(x3p, region = "exterior", offset = 0, ...)preProcess_crop(x3p, region = "exterior", offset = 0, ...)
x3p |
an x3p object containing the surface matrix of a cartridge case scan |
region |
dictates whether the observations on the "exterior" or "interior" of the scan are removed |
offset |
an integer (positive or negative) value to add to the estimated radius of the associated region |
... |
internal usage |
An x3p object containing the surface matrix of a breech face impression scan where the observations on the exterior/interior of the breech face scan surface.
#Process fadul1.1 "from scratch" (takes > 5 seconds to run) ## Not run: nbtrd_link <- "https://tsapps.nist.gov/NRBTD/Studies/CartridgeMeasurement/" fadul1.1_link <- "DownloadMeasurement/2d9cc51f-6f66-40a0-973a-a9292dbee36d" fadul1.1 <- x3ptools::read_x3p(paste0(nbtrd_link,fadul1.1_link)) fadul1.1_extCropped <- preProcess_crop(x3p = fadul1.1, radiusOffset = -30, region = "exterior") fadul1.1_extIntCropped <- preProcess_crop(x3p = fadul1.1_extCropped, radiusOffset = 200, region = "interior") x3pListPlot(list("Original" = fadul1.1, "Exterior Cropped" = fadul1.1_extCropped, "Exterior & Interior Cropped" = fadul1.1_extIntCropped )) ## End(Not run)#Process fadul1.1 "from scratch" (takes > 5 seconds to run) ## Not run: nbtrd_link <- "https://tsapps.nist.gov/NRBTD/Studies/CartridgeMeasurement/" fadul1.1_link <- "DownloadMeasurement/2d9cc51f-6f66-40a0-973a-a9292dbee36d" fadul1.1 <- x3ptools::read_x3p(paste0(nbtrd_link,fadul1.1_link)) fadul1.1_extCropped <- preProcess_crop(x3p = fadul1.1, radiusOffset = -30, region = "exterior") fadul1.1_extIntCropped <- preProcess_crop(x3p = fadul1.1_extCropped, radiusOffset = 200, region = "interior") x3pListPlot(list("Original" = fadul1.1, "Exterior Cropped" = fadul1.1_extCropped, "Exterior & Interior Cropped" = fadul1.1_extIntCropped )) ## End(Not run)
performs the morphological operations and dilation to "shave" observations off of the interior or exterior of a cartridge case surface matrix.
preProcess_erode(x3p, region, morphRadius = 50)preProcess_erode(x3p, region, morphRadius = 50)
x3p |
an x3p object |
region |
either "interior," meaning the observations around the firing pin hole will be eroded, or "exterior," meaning the observations around the outer edge of the cartridge case primer will be eroded |
morphRadius |
controls the amount of erosion. Larger values correspond to a larger (circular) morphological mask leading to more erosion. |
Performs a low, high, or bandpass Gaussian filter on a surface matrix with a particular cut-off wavelength.
preProcess_gaussFilter(x3p, wavelength = c(16, 500), filtertype = "bp")preProcess_gaussFilter(x3p, wavelength = c(16, 500), filtertype = "bp")
x3p |
an x3p object containing a surface matrix |
wavelength |
cut-off wavelength |
filtertype |
specifies whether a low pass, "lp", high pass, "hp", or bandpass, "bp" filter is to be used. Note that setting filterype = "bp" means that wavelength should be a vector of two numbers. In this case, the max of these two number will be used for the high pass filter and the min for the low pass filter. |
An x3p object containing the Gaussian-filtered surface matrix.
https://www.mathworks.com/matlabcentral/fileexchange/61003-filt2-2d-geospatial-data-filter?focused=7181587&tab=example
data(fadul1.1_processed) #Applying the function to fadul1.1_processed (note that this scan has already # been Gaussian filtered) cmcR::preProcess_gaussFilter(fadul1.1_processed) #As a part of the recommended preprocessing pipeline (take > 5 sec to run): ## Not run: nbtrd_link <- "https://tsapps.nist.gov/NRBTD/Studies/CartridgeMeasurement/" fadul1.1_link <- "DownloadMeasurement/2d9cc51f-6f66-40a0-973a-a9292dbee36d" fadul1.1 <- x3ptools::read_x3p(paste0(nbtrd_link,fadul1.1_link)) fadul1.1_extCropped <- preProcess_crop(x3p = fadul1.1, region = "exterior", radiusOffset = -30) fadul1.1_intCroped <- preProcess_crop(x3p = fadul1.1_extCropped, region = "interior", radiusOffset = 200) fadul1.1_leveled <- preProcess_removeTrend(x3p = fadul1.1_intCroped, statistic = "quantile", tau = .5, method = "fn") fadul1.1_filtered <- preProcess_gaussFilter(x3p = fadul1.1_leveled, wavelength = c(16,500), filtertype = "bp") x3pListPlot(list("Original" = fadul1.1, "Ext. & Int. Cropped" = fadul1.1_intCroped, "Cropped and Leveled" = fadul1.1_leveled, "Filtered" = fadul1.1_filtered),type = "list") ## End(Not run)data(fadul1.1_processed) #Applying the function to fadul1.1_processed (note that this scan has already # been Gaussian filtered) cmcR::preProcess_gaussFilter(fadul1.1_processed) #As a part of the recommended preprocessing pipeline (take > 5 sec to run): ## Not run: nbtrd_link <- "https://tsapps.nist.gov/NRBTD/Studies/CartridgeMeasurement/" fadul1.1_link <- "DownloadMeasurement/2d9cc51f-6f66-40a0-973a-a9292dbee36d" fadul1.1 <- x3ptools::read_x3p(paste0(nbtrd_link,fadul1.1_link)) fadul1.1_extCropped <- preProcess_crop(x3p = fadul1.1, region = "exterior", radiusOffset = -30) fadul1.1_intCroped <- preProcess_crop(x3p = fadul1.1_extCropped, region = "interior", radiusOffset = 200) fadul1.1_leveled <- preProcess_removeTrend(x3p = fadul1.1_intCroped, statistic = "quantile", tau = .5, method = "fn") fadul1.1_filtered <- preProcess_gaussFilter(x3p = fadul1.1_leveled, wavelength = c(16,500), filtertype = "bp") x3pListPlot(list("Original" = fadul1.1, "Ext. & Int. Cropped" = fadul1.1_intCroped, "Cropped and Leveled" = fadul1.1_leveled, "Filtered" = fadul1.1_filtered),type = "list") ## End(Not run)
Finds plane of breechface marks using the RANSAC method
preProcess_ransacLevel( x3p, ransacInlierThresh = 1e-06, ransacFinalSelectThresh = 2e-05, iters = 300, returnResiduals = TRUE )preProcess_ransacLevel( x3p, ransacInlierThresh = 1e-06, ransacFinalSelectThresh = 2e-05, iters = 300, returnResiduals = TRUE )
x3p |
an x3p object containing a surface matrix |
ransacInlierThresh |
threshold to declare an observed value close to the fitted plane an "inlier". A smaller value will yield a more stable estimate. |
ransacFinalSelectThresh |
once the RANSAC plane is fitted based on the ransacInlierThresh, this argument dictates which observations are selected as the final breech face estimate. |
iters |
number of candidate planes to fit (higher value yields more stable breech face estimate) |
returnResiduals |
dictates whether the difference between the estimated breech face and fitted plane are returned (residuals) or if the estimates breech face is simply shifted down by its mean value |
an x3p object containing the leveled surface matrix.
Given input depths (in microns), find best-fitting plane using RANSAC. This should be the plane that the breechface marks are on. Adapted from cartridges3D::findPlaneRansac function. This a modified version of the findPlaneRansac function available in the cartridges3D package on GitHub.
The preProcess_ransacLevel function will throw an error if the final plane estimate is rank-deficient (which is relatively unlikely, but theoretically possible). Re-run the function (possibly setting a different seed) if this occurs.
https://github.com/xhtai/cartridges3D
## Not run: nbtrd_link <- "https://tsapps.nist.gov/NRBTD/Studies/CartridgeMeasurement/" fadul1.1_link <- "DownloadMeasurement/2d9cc51f-6f66-40a0-973a-a9292dbee36d" fadul1.1 <- x3ptools::read_x3p(paste0(nbtrd_link,fadul1.1_link)) fadul1.1_ransacLeveled <- fadul1.1 %>% preProcess_crop(region = "exterior", radiusOffset = -30) %>% preProcess_crop(region = "interior", radiusOffset = 200) %>% preProcess_removeTrend(statistic = "quantile", tau = .5, method = "fn") x3pListPlot(list("Original" = fadul1.1, "RANSAC Leveled" = fadul1.1_ransacLeveled),type = "list") ## End(Not run)## Not run: nbtrd_link <- "https://tsapps.nist.gov/NRBTD/Studies/CartridgeMeasurement/" fadul1.1_link <- "DownloadMeasurement/2d9cc51f-6f66-40a0-973a-a9292dbee36d" fadul1.1 <- x3ptools::read_x3p(paste0(nbtrd_link,fadul1.1_link)) fadul1.1_ransacLeveled <- fadul1.1 %>% preProcess_crop(region = "exterior", radiusOffset = -30) %>% preProcess_crop(region = "interior", radiusOffset = 200) %>% preProcess_removeTrend(statistic = "quantile", tau = .5, method = "fn") x3pListPlot(list("Original" = fadul1.1, "RANSAC Leveled" = fadul1.1_ransacLeveled),type = "list") ## End(Not run)
Given a surface matrix, estimates and filters any pixels within the estimated firing pin impression circle
preProcess_removeFPCircle( x3p, aggregationFunction = mean, smootherSize = 2 * round((0.1 * nrow(surfaceMat)/2)) + 1, gridSize = 40, gridGranularity = 1, houghScoreQuant = 0.9 )preProcess_removeFPCircle( x3p, aggregationFunction = mean, smootherSize = 2 * round((0.1 * nrow(surfaceMat)/2)) + 1, gridSize = 40, gridGranularity = 1, houghScoreQuant = 0.9 )
x3p |
an x3p object containing a surface matrix |
aggregationFunction |
function to select initial radius estimate from those calculated using fpRadiusGridSearch |
smootherSize |
size of average smoother (to be passed to zoo::roll_mean) |
gridSize |
size of grid, centered on the initial radius estimate, to be used to determine the best fitting circle to the surface matrix via the Hough transform method |
gridGranularity |
granularity of radius grid used to determine the best fitting circle to the surface matrix via the Hough transform method |
houghScoreQuant |
quantile cut-off to be used when determining a final radius estimate using the score values returned by the imager::hough_circle |
An x3p object containing a surface matrix with the estimated firing pin circle pixels replaced with NAs.
imager treats a matrix as its transpose (i.e., x and y axes are swapped). As such, relative to the original surface matrix, the x and y columns in the data frame fpImpressionCircle actually correspond to the row and column indices at which the center of the firing pin impression circle is estiamted to be.
## Not run: nbtrd_link <- "https://tsapps.nist.gov/NRBTD/Studies/CartridgeMeasurement/" fadul1.1_link <- "DownloadMeasurement/2d9cc51f-6f66-40a0-973a-a9292dbee36d" fadul1.1 <- x3ptools::read_x3p(paste0(nbtrd_link,fadul1.1_link)) fadul1.1_labelCropped <- fadul1.1 %>% preProcess_crop(region = "exterior", radiusOffset = -30) %>% preProcess_crop(region = "interior", radiusOffset = 200) %>% preProcess_removeTrend(statistic = "quantile", tau = .5, method = "fn") fadul1.1_houghCropped <- fadul1.1 %>% x3ptools::x3p_sample() %>% preProcess_ransacLevel() %>% preProcess_crop(region = "exterior", radiusOffset = -30) %>% preProcess_removeFPCircle() x3pListPlot(list("Original" = fadul1.1, "Cropped by Labeling" = fadul1.1_labelCropped, "Cropped by Hough" = fadul1.1_houghCropped),type = "list") ## End(Not run)## Not run: nbtrd_link <- "https://tsapps.nist.gov/NRBTD/Studies/CartridgeMeasurement/" fadul1.1_link <- "DownloadMeasurement/2d9cc51f-6f66-40a0-973a-a9292dbee36d" fadul1.1 <- x3ptools::read_x3p(paste0(nbtrd_link,fadul1.1_link)) fadul1.1_labelCropped <- fadul1.1 %>% preProcess_crop(region = "exterior", radiusOffset = -30) %>% preProcess_crop(region = "interior", radiusOffset = 200) %>% preProcess_removeTrend(statistic = "quantile", tau = .5, method = "fn") fadul1.1_houghCropped <- fadul1.1 %>% x3ptools::x3p_sample() %>% preProcess_ransacLevel() %>% preProcess_crop(region = "exterior", radiusOffset = -30) %>% preProcess_removeFPCircle() x3pListPlot(list("Original" = fadul1.1, "Cropped by Labeling" = fadul1.1_labelCropped, "Cropped by Hough" = fadul1.1_houghCropped),type = "list") ## End(Not run)
Level a breech face impression surface matrix by a conditional statistic
preProcess_removeTrend(x3p, statistic = "mean", ...)preProcess_removeTrend(x3p, statistic = "mean", ...)
x3p |
an x3p object containing the surface matrix of a cartridge case scan |
statistic |
either "mean" or "quantile" |
... |
arguments to be set in the quantreg::rq function if statistic = "quantile" is set. In this case, tau = .5 and method = "fn" are recommended |
an x3p object containing the leveled cartridge case scan surface matrix.
#Process fadul1.1 "from scratch" (takes > 5 seconds to run) ## Not run: nbtrd_link <- "https://tsapps.nist.gov/NRBTD/Studies/CartridgeMeasurement/" fadul1.1_link <- "DownloadMeasurement/2d9cc51f-6f66-40a0-973a-a9292dbee36d" fadul1.1 <- x3ptools::read_x3p(paste0(nbtrd_link,fadul1.1_link)) fadul1.1_extCropped <- preProcess_crop(x3p = fadul1.1, region = "exterior", radiusOffset = -30) fadul1.1_intCroped <- preProcess_crop(x3p = fadul1.1_extCropped, region = "interior", radiusOffset = 200) fadul1.1_leveled <- preProcess_removeTrend(x3p = fadul1.1_intCroped, statistic = "quantile", tau = .5, method = "fn") x3pListPlot(list("Original" = fadul1.1, "Ext. Cropped" = fadul1.1_extCropped, "Ext. & Int. Cropped" = fadul1.1_intCroped, "Cropped and Leveled" = fadul1.1_leveled)) ## End(Not run)#Process fadul1.1 "from scratch" (takes > 5 seconds to run) ## Not run: nbtrd_link <- "https://tsapps.nist.gov/NRBTD/Studies/CartridgeMeasurement/" fadul1.1_link <- "DownloadMeasurement/2d9cc51f-6f66-40a0-973a-a9292dbee36d" fadul1.1 <- x3ptools::read_x3p(paste0(nbtrd_link,fadul1.1_link)) fadul1.1_extCropped <- preProcess_crop(x3p = fadul1.1, region = "exterior", radiusOffset = -30) fadul1.1_intCroped <- preProcess_crop(x3p = fadul1.1_extCropped, region = "interior", radiusOffset = 200) fadul1.1_leveled <- preProcess_removeTrend(x3p = fadul1.1_intCroped, statistic = "quantile", tau = .5, method = "fn") x3pListPlot(list("Original" = fadul1.1, "Ext. Cropped" = fadul1.1_extCropped, "Ext. & Int. Cropped" = fadul1.1_intCroped, "Cropped and Leveled" = fadul1.1_leveled)) ## End(Not run)
Plots the surface matrices in a list of x3p objects. Either creates one plot faceted by surface matrix or creates individual plots per surface matrix and returns them in a list.
x3pListPlot( x3pList, type = "faceted", legend.quantiles = c(0, 0.01, 0.25, 0.5, 0.75, 0.99, 1), height.quantiles = c(0, 0.01, 0.025, 0.1, 0.25, 0.5, 0.75, 0.9, 0.975, 0.99, 1), height.colors = rev(c("#7f3b08", "#b35806", "#e08214", "#fdb863", "#fee0b6", "#f7f7f7", "#d8daeb", "#b2abd2", "#8073ac", "#542788", "#2d004b")), na.value = "gray65" )x3pListPlot( x3pList, type = "faceted", legend.quantiles = c(0, 0.01, 0.25, 0.5, 0.75, 0.99, 1), height.quantiles = c(0, 0.01, 0.025, 0.1, 0.25, 0.5, 0.75, 0.9, 0.975, 0.99, 1), height.colors = rev(c("#7f3b08", "#b35806", "#e08214", "#fdb863", "#fee0b6", "#f7f7f7", "#d8daeb", "#b2abd2", "#8073ac", "#542788", "#2d004b")), na.value = "gray65" )
x3pList |
a list of x3p objects. If the x3p objects are named in the list, then these names will be included in the title of their respective plot |
type |
dictates whether one plot faceted by surface matrix or a list of plots per surface matrix is returned. The faceted plot will have a consistent height scale across all surface matrices. |
legend.quantiles |
vector of quantiles to be shown as tick marks on legend plot |
height.quantiles |
vector of quantiles associated with each color defined in the height.colors argument |
height.colors |
vector of colors to be passed to scale_fill_gradientn that dictates the height value colorscale |
na.value |
color to be used for NA values (passed to scale_fill_gradientn) |
A ggplot object or list of ggplot objects showing the surface matrix height values.
data(fadul1.1_processed,fadul1.2_processed) x3pListPlot(list("Fadul 1-1" = fadul1.1_processed, "Fadul 1-2" = fadul1.2_processed))data(fadul1.1_processed,fadul1.2_processed) x3pListPlot(list("Fadul 1-1" = fadul1.1_processed, "Fadul 1-2" = fadul1.2_processed))