Hi SPHARM team,

Thank you very much for your excellent tool.

I'm aiming to run some correlations with covariates, however I'm running into some problems.

From my reading the Pearson & Spearman correlations only use uncorrected means, and this matches my trial runs where including covariates doesn't change the Pearson/Spearman parameter or significance maps. For example, both the command lines below give similar Pearson maps:
shapeAnalysisMANCOVA /path/to/inputfile.csv --interactionTest --simpleCorrs --numIndependent 3 --columnIndependent 1,2,3 --testColumn 1 
shapeAnalysisMANCOVA /path/to/inputfile.csv --interactionTest --simpleCorrs --numIndependent 1 --columnIndependent 1 --testColumn 1

What does change with the inclusion of covariates is the interaction test Raw P and FDR maps, however I'm not clear what exactly this interaction test is assessing as the significance maps don't overlap very well with areas of strong correlation on the Pearson R value maps.

Am I using the wrong command line options to use covariates for correlations, or is there something more fundamental to do with the interaction test/correlation difference I'm missing?

Thank you,
David.