Hi, doug. Thanks for your suggestion, but it does not seem to work. After having divided the ICV by 1e6, I reculated the betas, and got the similar results as before in Matlab, just the beta4 -- correspondent to ICV -- multiplied a 10^6. And then I computed the rank of matrix X that the value is 4, indicating that each 2 variables in design matrix do not highly correlated. Also I have finished an regress analysis including one class variable and one continuous variable using mri_glmfit, got the same result in Matlab. A wired situation... Is it any additional constrains in mri_glmfit when doing regression analysis?
Lijie Huang On Mon, May 2, 2011 at 9:09 PM, Douglas N Greve <gr...@nmr.mgh.harvard.edu>wrote: > I think the problem may be that your data are "badly scaled" meaning that > one column is much greater than another column. Try dividing the ICV by 1e6 > and see if you get more similar results. > > doug > > soft.join Huang wrote: > >> Doug, >> Thanks for your reply. >> I recalculated the beta value and got the same results as the Matlab does, >> but be different from the mri_glmfit's. I can not figure out where the >> problem is. >> Details following: >> I use the command >> mri_glmfit --table lh.volume.txt --fsgd volume.gender.fsgd doss >> --C gender.diff.mtx --glmdir lh.vol.glmdir >> to finish regression analysis. and got the beta value ( beta1 = >> 1.9781e+03, beta2 = 2.0639e+03, beta3 = 110.9220, beta4 = 5.1964e-04 ) from >> beta.mgh file. >> Using X.mat file and the same dependent variable Y.mat as >> mri_glmfit's, I got totally different results in Matlab( beta1 = 5.19e+02, >> beta2 = 4.44e+02, beta3 = 90.32, beta4 = 0.0015 ). >> I don't know how it happened... >> Hope for your reply. >> The data I used are all in the attachment. >> Best Regrads, >> Lijie Huang >> On Fri, Apr 29, 2011 at 9:45 PM, <gr...@nmr.mgh.harvard.edu <mailto: >> gr...@nmr.mgh.harvard.edu>> wrote: >> >> Lijie, >> >> I don't know what the matlab regress() function does. mri_glmfit >> is just >> solving the GLM equation, which you can do easily in matlab: >> >> beta = inv(X'*X)*X'*y; >> >> try that and see if you get the same as mri_glmfit. >> >> doug >> >> >> > Hi all, >> > >> > I'm working on a group analysis using mri_glmfit. >> > In analysis directory, I got a X.mat file which containing the >> matrix X ( >> > size 50x4 ) used to regress analysis, also the beta value from >> beta.mgh >> > file. >> > Strangely, I loaded the X.mat in Matlab and recalculated the >> beta using >> > function regress in matlab, but got a different result from that of >> > mri_glmfit. >> > I don't know where the problem is, is there any difference in >> algorithm >> > between mri_glmfit and matlab function regress( regress(Y,X) )? >> or I made >> > some mistakes during the procedure. >> > The X.mat file and the dependent variable Y.mat are in the >> attachment. >> > >> > Thanks in advance >> > >> > Lijie Huang >> > _______________________________________________ >> > Freesurfer mailing list >> > Freesurfer@nmr.mgh.harvard.edu >> <mailto: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. >> >> >> > -- > 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 > >
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