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Normalising covariance matrices

  • 15-11-2014 12:56pm
    #1
    Registered Users, Registered Users 2 Posts: 17,154 ✭✭✭✭


    I want to compare two groups on the size of their error ellipses in performing a precision judgement.

    The 2 dimensional precision allows me compute covariance matrices for each subject, which i then average for each group, and from these group averaged cov matrices I can extract the eigen values and vectors to construct the ellipses.

    However I am concerned that the subjects with worse precision are disproportionately dominanting the mean values, which is leading me towards a normalisation procedure for each subject's cov mat before I average.

    For this procedure I was thinking to dot divide each cell of each subject's cov mat with the determinant of their cov mat. Would this be appropriate?


Comments

  • Registered Users, Registered Users 2 Posts: 1,583 ✭✭✭alan4cult


    Could you work with the correlation matrix instead?


  • Registered Users, Registered Users 2 Posts: 17,154 ✭✭✭✭Neil3030


    I don't think so. Scaling all the variances to 1 would result in the analysis losing the ability to differentiate subjects on their precision.


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