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 <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 < ggonesc@upo.es 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 <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 ______________________________**_________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.**harvard.edu Freesurfer@nmr.mgh.harvard.edu> < Freesurfer@nmr.mgh.harvard.**edu Freesurfer@nmr.mgh.harvard.edu <mailto: Freesurfer@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
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