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 <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

        <ggonesc@upo.es <mailto:ggonesc@upo.es> <mailto: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>

            <mailto:greve@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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            --     --------------------------
            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: ggonesc@upo.es <mailto:ggonesc@upo.es>

        <mailto:ggonesc@upo.es <mailto:ggonesc@upo.es>>

        http://www.upo.es/neuroaging/es/
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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 <mailto:ltully@mednet.ucla.edu>

        <mailto:ltully@mednet.ucla.edu <mailto:ltully@mednet.ucla.edu>>
        ltully@fas.harvard.edu <mailto:ltully@fas.harvard.edu>
        <mailto:ltully@fas.harvard.edu <mailto:ltully@fas.harvard.edu>>

        310-267-0170 <tel: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 <mailto:greve@nmr.mgh.harvard.edu>
    Phone Number: 617-724-2358 <tel:617-724-2358>
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    <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html>

    Outgoing:
    ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/




--
--
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 <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
FileDrop: www.nmr.mgh.harvard.edu/facility/filedrop/index.html
Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/