`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

- 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")
}
```