cmcR (version 0.1.11)

decision_combineDirections: Combine data frames containing CMC results from 2 comparison directions

Description

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).

Usage

decision_combineDirections(
  reference_v_target_CMCs,
  target_v_reference_CMCs,
  corColName = "pairwiseCompCor",
  missingThetaDecision = "fail",
  compareThetas = TRUE,
  thetaThresh = 6
)

Value

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.

Arguments

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.

Examples

if (FALSE) {
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)
}