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)
>>
>>
>> _______________________________________________
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>> HCP-Users@humanconnectome.org
>> http://lists.humanconnectome.org/mailman/listinfo/hcp-users
>>
>
>
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