`decision_highCMC_cmcThetaDistrib.Rd`

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 )

cellIndex | vector/tibble column containing cell indices corresponding to a reference cell |
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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

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") }