Description Usage Arguments Value Note Examples

Compute CMC-theta distribution for a set of comparison features

1 2 3 4 5 6 7 8 9 10 | ```
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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
## 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)
``` |

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