Principle-Component-Analysis Statistics afterwards

This release contains a routine that helps to calculate if two groups are statistically different in the PC1/PC2 coordinate system


Updated 14 Sep 2022

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This is a summary of MATLAB tools I developed to facilitate PCA analysis

---Mahalanobis-Distance and getCovMatrices---- Main (Mahalanobis-Distance):
This is a tool to determine if there is a statistical difference between two subgroups in a PC1-PC2 coordinates system
It follows the routine demonstrated in
It includes the calculation of Mahalanobis Distance followed by F-test statistics
The program is designed for 2 variants (herein PC1 and PC2) and 2 subgroups (for example treatment and control group)
Input here is an Excel Table with following format
Columns: VAR1_group1 - VAR2_group1 - VAR1-group2 - VAR2_group2
(2nd col) (3. col) (4. col) (5. col)
herein VAR1 = PC1
VAR2 = PC2
User input: change in the code of MahalanobisDistance (main routine) the name of the sheet and insert number of groups (it's 2 as default, I recommend to leave that)
Output: DW = Mahalanobis Distance
Tsqr = two sample Tsquared
F = F-Value

Function getCovMatrices is called to calculate the pooled between-group covariance matrix (according to

Cite As

Eva-Maria Weiss (2023). Principle-Component-Analysis Statistics afterwards (, GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2019b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.