# Coordinate File (and T-Map) Group Difference

Determine if the differences between two groups of coordinate files are statistically significant.

## Inputs

• Two groups of Coordinate files..
• Fiducial Coordinate file.
• Open Topology file.
• Area Correction metric/shape file.
• Area Correction metric/shape file column number.
• Number of Iterations.  The number of iterations is limited to pow(2, N).
• Positive Threshold.
• Alpha (p-value that ranges zero to one).

## Outputs

• Metric/Shape file containing the distances computed from the input Coordinate files.  This file contains one the distance and the X/Y/Z distance components.
• Permutation Distance Metric/Shape file.  This file contains number of iterations columns.
• Paint file identifying clusters.
• Text report.

## Algorithm

• Create average coordinate files for both of the input groups.
• Compute deviations for both of the average coordinate files.  The deviations are derived from the distance between the average coordinate and each of the individual's coordinates.
• (Coord Diff Only) Create the difference Metric/Shape file where the first column is the distance between the two average coordinate files and columns two, three, and four are the X/Y/Z distance components.
• (T-Map Coord Diff Only) Create the difference Metric/Shape file where first column is a T map where T = (distance between average coords / (Average Coord 1 Deviation + Average Coord 2 Deviation)).
• Create a shuffled coordinate file where the number of columns is the number of iterations entered by the user and use this shuffled coordinate file to create the shuffled distance file.
• Find the biggest cluster in each column of the shuffled distance file.
• Find the largest (alpha)(iterations) clusters in the shuffled distance file and use this value as the cutoff in the distance file.
• Report all clusters and create the paint file showing the clusters.