decision_highCMC_cmcThetaDistrib: Compute CMC-theta distribution for a set of comparison...

In cmcR: An Implementation of the 'Congruent Matching Cells' Method

Compute CMC-theta distribution for a set of comparison features

Description

Compute CMC-theta distribution for a set of comparison features

Usage

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

## 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)

cmcR documentation built on Dec. 10, 2022, 5:06 p.m.