Hi Doug,
I gave that a try but got a matrix error - below is the command line and
error output from matlab, can you spot what I'm doing wrong? (I'm a matlab
newbie I'm afraid...)
y = load('contrast/cache.th13.abs.y.ocn.dat');
X = load('Xg.dat');
beta = inv(X'*X)*X'*y;
C = load('contrast/C.dat');
rvar = MRIread('rvar.mgh');
betamat = fast_vol2mat(beta);
rvarmat = fast_vol2mat(rvar);
rhomat = fast_glm_pcc(betamat,X,C,rvarmat);
rho = beta;
rho.vol = fast_mat2vol(rhomat,rho.volsize);
MRIwrite(rho,'contrast/pcc.mgh')
??? Error using ==> mtimes
Inner matrix dimensions must agree.
Error in ==> fast_glm_pcc at 39
yhatd = Rd*(X*beta);
Thanks!
Laura
On Thu, Mar 21, 2013 at 10:56 AM, Douglas N Greve <gr...@nmr.mgh.harvard.edu
> wrote:
> It does it across the whole brain (on a voxel-by-voxel basis) but it is
> specific to the contrasts that you specify. You can run the same commands
> on the cluster averaged data (xxx.y.ocn...). To do this, don't load in the
> beta file. Instead
>
> y = load('xxx.y.ocn...');
> beta = inv(X'*X)*X'*y;
> then proceed with the rest of the code
>
> doug
>
>
>
> On 03/21/2013 01:49 PM, Laura M. Tully wrote:
>
>> I had this exact question yesterday :-) The functions necessary to run it
>> are in $FREESURFER_HOME/matlab and $FREESURFER_HOME/fsfast/**toolbox
>> Just add the path to these directories in your matlab (file -> setpath)
>> and you can run the function from your matlab command window
>>
>> *Doug -* I had a follow up question about using this function on clusters
>> identified in mri_glmfit-sim. Is the function as written below calculating
>> the pcc across the whole brain or is it just calculating the rho using the
>> betas specific to the cluster found in mri_glmfit-sim by only taking those
>> betas from beta.mgh that are specific to the contrast and cluster results
>> in C.dat? The reason I ask is that I have a cluster showing a significant
>> relationship between thickness and a continuous behavioral variable (a
>> positive relationship as denoted by a positive log10p of the Maxvertex in
>> the cluster) and I'd like to get the correlation coefficient for it but
>> when I run the PCC function I get zero so I'm wondering if this is because
>> it is calculating across the whole brain rather than just within the
>> cluster? (btw I get the same issue with a similar relationship showing
>> positive corr between thickness and two behav variables).
>> Thanks,
>>
>> Laura.
>>
>>
>>
>> On Thu, Mar 21, 2013 at 10:37 AM, Gabriel Gonzalez Escamilla <
>> ggon...@upo.es <mailto:ggon...@upo.es>> wrote:
>>
>> Thanks Doug for your quick answer,
>>
>> Sorry for so late answer.
>>
>> One question about this, is about the fast_vol2mat, is this a
>> function? if so, where can I get it?
>>
>> As the PCC is the R value, I'm guessing that I can just square at
>> it, to obtain R2.
>>
>> When you asked me to divide the beta by sqrt(rvar), is there any
>> place where I can find is this is the correct way to get the
>> standardized beta?
>>
>> Best regards,
>> Gabriel
>>
>>
>>
>> El 14/03/13, *Douglas Greve * <gr...@nmr.mgh.harvard.edu
>> <mailto:gr...@nmr.mgh.harvard.**edu <gr...@nmr.mgh.harvard.edu>>>
>> escribió:
>>
>>>
>>> Hi Gabriel, I've attached a matlab routine which will compute the
>>> PCC. If you cd into the GLM dir, then
>>>
>>> X = load('Xg.dat');
>>> beta = MRIread('beta.mgh');
>>> C = load('yourcontrast/C.dat');
>>> rvar = MRIread('rvar.mgh');
>>>
>>> betamat = fast_vol2mat(beta);
>>> rvarmat = fast_vol2mat(rvar);
>>>
>>> rhomat = fast_glm_pcc(betamat,X,C,**rvarmat);
>>>
>>> rho = beta;
>>> rho.vol = fast_mat2vol(rhomat,rho.**volsize);
>>> MRIwrite(rho,'yourcontrast/**pcc.mgh')
>>>
>>> The R2 should just be the square of the PCC, right?
>>>
>>> For the standardized beta, do you just divide the beta by sqrt(rvar)?
>>>
>>> doug
>>>
>>>
>>> On 3/14/13 1:39 PM, Gabriel Gonzalez Escamilla wrote:
>>>
>>>>
>>>> Dear Freesurfers
>>>>
>>>> I'm performing regression analyses including confounding
>>>> variables, and I would like to know how to obtain the following
>>>> information:
>>>>
>>>> A) The squre R
>>>>
>>>> and
>>>>
>>>> B) The standarized beta coefficient of an independient variable;
>>>> and the partial correlation with its p-values
>>>>
>>>>
>>>> Many thanks in advanced,
>>>> Gabriel
>>>>
>>>>
>>>>
>>>> ______________________________**_________________
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>>>> freesur...@nmr.mgh.**harvard.edu <Freesurfer@nmr.mgh.harvard.edu>>>
>>>>
>>>> https://mail.nmr.mgh.harvard.**edu/mailman/listinfo/**freesurfer<https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer>
>>>>
>>>
>>>
>> -- --------------------------
>> PhD. student Gabriel González-Escamilla
>> Laboratory of Functional Neuroscience
>> Department of Physiology, Anatomy, and Cell Biology
>> University Pablo de Olavide
>> Ctra. de Utrera, Km.1
>> 41013 - Seville
>> - Spain -
>>
>> Email: ggon...@upo.es <mailto:ggon...@upo.es>
>>
>> http://www.upo.es/neuroaging/**es/ <http://www.upo.es/neuroaging/es/>
>> ______________________________**_________________
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>>
>> The information in this e-mail is intended only for the person to
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>> --
>> --
>> Laura M. Tully, MA
>> Social Neuroscience & Psychopathology, Harvard University
>> Center for the Assessment and Prevention of Prodromal States, UCLA Semel
>> Institute of Neuroscience
>> ltu...@mednet.ucla.edu <mailto:ltu...@mednet.ucla.edu**>
>> ltu...@fas.harvard.edu <mailto:ltu...@fas.harvard.edu**>
>>
>> 310-267-0170
>> --
>> My musings as a young clinical scientist: http://theclinicalbrain.**
>> blogspot.com/ <http://theclinicalbrain.blogspot.com/>
>> Follow me on Twitter: @tully_laura
>>
>
> --
> Douglas N. Greve, Ph.D.
> MGH-NMR Center
> gr...@nmr.mgh.harvard.edu
> Phone Number: 617-724-2358
> Fax: 617-726-7422
>
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>
>
--
--
Laura M. Tully, MA
Social Neuroscience & Psychopathology, Harvard University
Center for the Assessment and Prevention of Prodromal States, UCLA Semel
Institute of Neuroscience
ltu...@mednet.ucla.edu
ltu...@fas.harvard.edu
310-267-0170
--
My musings as a young clinical scientist:
http://theclinicalbrain.blogspot.com/
Follow me on Twitter: @tully_laura
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