cmcR (version 0.1.11)

decision_CMC: Applies the decision rules of the original method of Song (2013) or the High CMC method of Tong et al. (2015)

Description

Applies the decision rules of the original method of Song (2013) or the High CMC method of Tong et al. (2015)

Usage

decision_CMC(
  cellIndex,
  x,
  y,
  theta,
  corr,
  xThresh = 20,
  yThresh = xThresh,
  thetaThresh = 6,
  corrThresh = 0.5,
  tau = NULL
)

Value

A vector of the same length as the input containing the CMC classification under one of the two decision rules.

Arguments

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.

See Also

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

Examples

if (FALSE) {
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")
}