Re: [Freesurfer] Stat doubts
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,GabrielEl 14/03/13, Douglas Greve 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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-- --PhD. student Gabriel González-EscamillaLaboratory of Functional NeuroscienceDepartment of Physiology, Anatomy, and Cell BiologyUniversity Pablo de OlavideCtra. de Utrera, Km.141013 - Seville- Spain -Email: ggon...@upo.eshttp://www.upo.es/neuroaging/es/
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Re: [Freesurfer] Stat doubts
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
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 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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Freesurfer@nmr.mgh.harvard.eduhttps://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
http://www.upo.es/neuroaging/es/
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is
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e-mail
contains patient information, please contact the Partners Compliance
HelpLine at
http://www.partners.org/complianceline . If the e-mail was sent to you in
error
but does not contain patient information, please contact the sender and
properly
dispose of the e-mail.
--
--
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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Re: [Freesurfer] Stat doubts
On 03/21/2013 01:37 PM, Gabriel Gonzalez Escamilla 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?
$FREESURFER_HOME/fsfast/toolbox
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?
I don't know. Maybe ask the person who asked you to get the standardized
beta what the definition is:).
doug
Best regards,
Gabriel
El 14/03/13, *Douglas Greve * 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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Freesurfer@nmr.mgh.harvard.edu Freesurfer@nmr.mgh.harvard.edu
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
http://www.upo.es/neuroaging/es/
--
Douglas N. Greve, Ph.D.
MGH-NMR Center
gr...@nmr.mgh.harvard.edu
Phone Number: 617-724-2358
Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
FileDrop: www.nmr.mgh.harvard.edu/facility/filedrop/index.html
Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
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The information in this e-mail is intended only for the person to whom it is
addressed. If you believe this e-mail was sent to you in error and the e-mail
contains patient information, please contact the Partners Compliance HelpLine at
http://www.partners.org/complianceline . If the e-mail was sent to you in error
but does not contain patient information, please contact the sender and properly
dispose of the e-mail.
Re: [Freesurfer] Stat doubts
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 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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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/
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The information in this e-mail is intended only for the person to
whom it is
addressed. If you believe this e-mail was sent to you in error and
the e-mail
contains patient information, please contact the Partners
Compliance HelpLine at
http://www.partners.org/complianceline . If the e-mail was sent to
you in error
but does not contain patient information, please contact the
sender and properly
dispose of the e-mail.
--
--
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
Re: [Freesurfer] Stat doubts
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
__**_
Freesurfer mailing list
Freesurfer@nmr.mgh.harvard.edu mailto:freesur...@nmr.mgh.**
harvard.edu Freesurfer@nmr.mgh.harvard.edu
freesur...@nmr.mgh.harvard.**edu Freesurfer@nmr.mgh.harvard.edu mailto:
freesur...@nmr.mgh.**harvard.edu Freesurfer@nmr.mgh.harvard.edu
https://mail.nmr.mgh.harvard.**edu/mailman/listinfo/**freesurferhttps://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
Re: [Freesurfer] Stat doubts
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
On Thu, Mar 21, 2013 at 10:56 AM, Douglas N Greve
gr...@nmr.mgh.harvard.edu mailto: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 mailto: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
mailto:gr...@nmr.mgh.harvard.edu
mailto: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
Re: [Freesurfer] Stat doubts
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
gr...@nmr.mgh.harvard.eduwrote:
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
On Thu, Mar 21, 2013 at 10:56 AM, Douglas N Greve
gr...@nmr.mgh.harvard.edu
mailto:gr...@nmr.mgh.harvard.**edugr...@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 mailto: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
mailto:gr...@nmr.mgh.harvard.**edugr...@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);
Re: [Freesurfer] Stat doubts
yes, no need to load rvar or the first computations of betamat and
rvarmat, and you don't need beta = ...
On 3/21/13 6:34 PM, Laura M. Tully wrote:
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
gr...@nmr.mgh.harvard.edu mailto:gr...@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
On Thu, Mar 21, 2013 at 10:56 AM, Douglas N Greve
gr...@nmr.mgh.harvard.edu mailto:gr...@nmr.mgh.harvard.edu
mailto:gr...@nmr.mgh.harvard.edu
mailto: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
mailto:ggon...@upo.es mailto:ggon...@upo.es
mailto:ggon...@upo.es mailto:ggon...@upo.es
mailto: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.
[Freesurfer] Stat doubts
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 Rand B) The standarized beta coefficient of an independient variable; and the partial correlation with its p-valuesMany thanks in advanced,Gabriel ___ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
Re: [Freesurfer] Stat doubts
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
___
Freesurfer mailing list
Freesurfer@nmr.mgh.harvard.edu
https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
function rho = fast_glm_pcc(beta,X,C,rvar)
% rho = fast_glm_pcc(beta,X,C,rvar)
%
% Computes the partial correlation coefficient of the given
% univariate contrast.
%
% WARNING: X must have a column of 1s or both X and y must have
% been demeaned before the glm.
%
% $Id: fast_glm_pcc.m,v 1.2 2012/11/19 22:18:23 greve Exp $
if(nargin ~= 4)
fprintf('rho = fast_glm_pcc(beta,X,C,rvar)\n');
return;
end
[J nbeta] = size(C);
if(J ~= 1)
fprintf('ERROR: fast_glm_pcc: contrast must be univariate\n');
return;
end
ntp = size(X,1);
DOF = ntp - nbeta;
% Design matrix projected onto the contrast space. This forms
% the space of the contrast
Xc = X*C';
% Null space of the contrast
D = null(C);
% Design matrix projected onto the null-space contrast. This forms
% the space of the nuisance regressors
Xd = X*D;
% Residual-forming matrix of the nuisance regressors
Rd = eye(ntp) - Xd*inv(Xd'*Xd)*Xd';
% Orthogonalize both Xc and yhat wrt the nuisance regressors
Xcd = Rd*Xc;
yhatd = Rd*(X*beta);
% Compute sums and cross-products needed for correlation coef
Xcdtyz = Xcd'*yhatd;
sumXcd = sum(Xcd);
sumyz = sum(yhatd); % Fails without column of 1s in X
XcdtXcd = sum(Xcd.^2);
yztyz = sum(yhatd.^2) + DOF*rvar;
rho = (Xcdtyz-sumXcd.*sumyz)./sqrt((XcdtXcd-sumXcd.^2).*(yztyz-sumyz.^2));
return;
___
Freesurfer mailing list
Freesurfer@nmr.mgh.harvard.edu
https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
The information in this e-mail is intended only for the person to whom it is
addressed. If you believe this e-mail was sent to you in error and the e-mail
contains patient information, please contact the Partners Compliance HelpLine at
http://www.partners.org/complianceline . If the e-mail was sent to you in error
but does not contain patient information, please contact the sender and properly
dispose of the e-mail.
