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
)

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

corrThresh

to classify particular correlation values "congruent" (conditional on a particular theta value) if they are at least corrThresh

Value

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.

Note

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

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(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") }