So this instead?
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);[betamat rvarmat] = fast_glmfit(y,X);
rhomat = fast_glm_pcc(betamat,X,C,rvarmat);
rho = beta;
rho.vol = fast_mat2vol(rhomat,rho.volsize);
MRIwrite(rho,'contrast/pcc.mgh')
On Thu, Mar 21, 2013 at 3:14 PM, Douglas N Greve <greve@nmr.mgh.harvard.edu> wrote:
oh, sorry, you'll need to recompute rvarmat (don't load rvar or beta)
[betamat rvarmat] = fast_glmfit(y,X);
Make sure you have $FREESURFER_HOME/fsfast/toolbox in your matlab path
doug
On 03/21/2013 05:04 PM, Laura M. Tully wrote:
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
<ggonesc@upo.es <mailto:ggonesc@upo.es> <mailto:ggonesc@upo.esOn Thu, Mar 21, 2013 at 10:56 AM, Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@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
<mailto:greve@nmr.mgh.harvard.edu
<mailto:ggonesc@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 * <greve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.edu>
<mailto:Freesurfer@nmr.mgh.harvard.edu
<mailto:greve@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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-- My musings as a young clinical scientist:
http://theclinicalbrain.blogspot.com/
Follow me on Twitter: @tully_laura
-- Douglas N. Greve, Ph.D.
MGH-NMR Center
Phone Number: 617-724-2358 <tel:617-724-2358>
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--
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Social Neuroscience & Psychopathology, Harvard University
Center for the Assessment and Prevention of Prodromal States, UCLA Semel Institute of Neuroscience
ltully@mednet.ucla.edu <mailto:ltully@mednet.ucla.edu>
ltully@fas.harvard.edu <mailto:ltully@fas.harvard.edu>
310-267-0170
--
My musings as a young clinical scientist: http://theclinicalbrain.blogspot.com/
Follow me on Twitter: @tully_laura
--
Douglas N. Greve, Ph.D.
MGH-NMR Center
greve@nmr.mgh.harvard.edu
Phone Number: 617-724-2358
Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
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--
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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
ltully@mednet.ucla.edu
ltully@fas.harvard.edu
310-267-0170
--My musings as a young clinical scientist: http://theclinicalbrain.blogspot.com/Follow me on Twitter: @tully_laura