Thank you for the response. I am, too, confused by some being non-zero finite values, and others being infinities.
Before computing a correlation matrix, if standardized by subtracting the mean and scaling by variance, all diagonal elements should be exactly 1. What I am concerned about is how the whole matrix was computed, since a fundamental characteristic of correlation matrix is not satisfied Best, Sang On Mon, May 15, 2017 at 11:33 AM Timothy Coalson <tsc...@mst.edu> wrote: > Per the name "zcorr", the correlation values have been z-transformed > (fisher's small z transform). I am somewhat confused as to why some > elements in the diagonal are not infinite. The "true" value of applying > this transform would be infinite on the entire diagonal, as arctanh(1) is > infinite. I am guessing this result was generated in matlab, as wb_command > actually prevents infinities when using the z transform, putting a cap on > the correlation (when not using z-transform, it shows correlations of 1 as > expected). > > Whatever analysis you do with correlation matrices like this should ignore > the diagonal anyway, since it is correlation to itself. > > Tim > > > On Mon, May 15, 2017 at 3:57 AM, Sang-Yun Oh <san...@gmail.com> wrote: > >> I downloaded group average functional correlation >> file: HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii >> >> Some diagonal elements of the square matrix (91282x91282) are infinite >> (Please see below). >> >> I want to use this matrix in ananalysis; however, I am not sure how to >> understand or deal with infinite diagonal values. >> >> I appreciate any insight >> >> Thanks, >> Sang >> >> ====================== >> >> In [1]: import nibabel >> >> In [2]: asdf = >> nibabel.load('HCP_S900_820_rfMRI_MSMAll_groupPCA_d4500ROW_zcorr.dconn.nii') >> >> In [3]: img = asdf.get_data() >> >> In [4]: img.shape >> Out[4]: (1, 1, 1, 1, 91282, 91282) >> >> In [5]: S = img[0,0,0,0,:,:] >> >> In [6]: S >> Out[6]: >> memmap([[ 8.66434002e+00, 1.96847185e-01, 1.66294336e-01, ..., >> 1.01449557e-01, 7.45474100e-02, 1.15624115e-01], >> [ 1.96847185e-01, inf, 3.36383432e-01, ..., >> -5.70017472e-03, -5.49946353e-02, 3.72834280e-02], >> [ 1.66294336e-01, 3.36383432e-01, inf, ..., >> -4.45242636e-02, -6.07097335e-02, -1.51601573e-02], >> ..., >> [ 1.01449557e-01, -5.70017472e-03, -4.45242636e-02, ..., >> inf, 1.91883039e+00, 9.20160294e-01], >> [ 7.45474100e-02, -5.49946353e-02, -6.07097335e-02, ..., >> 1.91883111e+00, 8.31776619e+00, 8.82132888e-01], >> [ 1.15624115e-01, 3.72833721e-02, -1.51601573e-02, ..., >> 9.20160294e-01, 8.82132888e-01, 8.66434002e+00]], >> dtype=float32) >> >> In [7]: S.diagonal() >> Out[7]: >> memmap([ 8.66434002, inf, inf, ..., inf, >> 8.31776619, 8.66434002], dtype=float32) >> >> >> _______________________________________________ >> HCP-Users mailing list >> HCP-Users@humanconnectome.org >> http://lists.humanconnectome.org/mailman/listinfo/hcp-users >> > > _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users