OK, here is my 2 cents. Since you mention that this is an interaction term
I wonder if the difference has to do with the centering. It may be that one
approach is subtracting out the mean and the other not. Also, are you
calculating the interaction term the same way?
Jason
On Fri, Jul 5, 2013 at 8:10 PM, Brian Roach <[log in to unmask]> wrote:
> Hi Donald,
>
> I checked some of our model parameters in the SPM.mat file, and I'm still
> not sure I understand the source of differences between the models.
> Everything that follows is based on SPM 5 code we're using to run this.
> Our SPM.xX.W (whitening matrix) is a sparse identity matrix so that when
> the spm_spm.m function whitens the data (lines 717-721):
>
> %-Whiten/Weight data and remove filter confounds
> %--------------------------------------------------------------
> fprintf('%s%30s',repmat(sprintf('\b'),1,30),'filtering')
>
> KWY = spm_filter(xX.K,W*Y);
>
> I think I should get values in KWY that are equal to my original data
> matrix Y (note: SPM.xX.K=1). Later, the betas and residual sum of squares
> are estimated (lines 732-739):
>
> %-General linear model: Weighted least squares estimation
> %--------------------------------------------------------------
> fprintf('%s%30s',repmat(sprintf('\b'),1,30),' estimation')
>
> beta = xX.pKX*KWY; %-Parameter estimates
> res = spm_sp('r',xX.xKXs,KWY); %-Residuals
> ResSS = sum(res.^2); %-Residual SSQ
> clear KWY %-Clear to save memory
>
> In the belly of spm_sp.m, the calculation of the 'res' variable is given
> as (line 1403):
>
> Y = Y - sX.u(:,[1:r])*(sX.u(:,[1:r])'*Y); % warning('route1');
>
> If I repeat this calculation for my test voxel:
>
> spmR = KWY - (SPM.xX.xKXs.u*SPM.xX.xKXs.u'*KWY)
>
> I obtain residual values that are identical to my SPSS residuals or
> residuals returned by the regress.m function in matlab. This is in
> addition to obtaining identical beta values with all three methods. The
> only other thing I can see happening to this ResSS variable, is scaling
> (lines 821-824):
>
> %-Write ResSS into ResMS (variance) image scaled by tr(RV) above
> %------------------------------------------------------------------
> if length(Q), jj(Q) = CrResSS; end
> VResMS = spm_write_plane(VResMS,jj,z);
>
> ...and lines 950-953:
> %-Set VResMS scalefactor as 1/trRV (raw voxel data is ResSS)
>
> %--------------------------------------------------------------------------
> VResMS.pinfo(1) = 1/xX.trRV;
> VResMS = spm_create_vol(VResMS);
>
> and based on that scaling variable (SPM.xX.trRV), I get a residual mean
> square value equal to that returned by SPSS or regress.m, but not equal to
> the actual value I see in the ResMS.img generated by running the spm job.
> What am I missing?
>
> thanks,
> Brian
>
>
>
> On 7/5/13 10:35 AM, MCLAREN, Donald wrote:
>
> It's a combination of both. The REML estimates are used to create a
> whitening matrix, which is multiplied by the design matrix to get the
> matrix that is ultimately estimated. The whitening doesn't change the
> relationship of the betas between conditions or columns, but it does change
> the residuals. Hence, the contrasts are the same, but the statistics are
> different.
>
> Best Regards, Donald McLaren
> =================
> D.G. McLaren, Ph.D.
> Research Fellow, Department of Neurology, Massachusetts General Hospital
> and
> Harvard Medical School
> Postdoctoral Research Fellow, GRECC, Bedford VA
> Website: http://www.martinos.org/~mclaren
> Office: (773) 406-2464
> =====================
> This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED
> HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is
> intended only for the use of the individual or entity named above. If the
> reader of the e-mail is not the intended recipient or the employee or agent
> responsible for delivering it to the intended recipient, you are hereby
> notified that you are in possession of confidential and privileged
> information. Any unauthorized use, disclosure, copying or the taking of any
> action in reliance on the contents of this information is strictly
> prohibited and may be unlawful. If you have received this e-mail
> unintentionally, please immediately notify the sender via telephone at
> (773)
> 406-2464 or email.
>
>
> On Fri, Jul 5, 2013 at 1:31 PM, Mathalon, Daniel <[log in to unmask]
> > wrote:
>
>> Thanks. Do you mean correction for unequal variances between groups? Or
>> is it the fact that SPM uses ReML to estimate variance whereas SPSS uses
>> least-squares approach? Or perhaps both contribute?
>>
>> Dan
>>
>> On Jul 5, 2013, at 8:15 AM, MCLAREN, Donald wrote:
>>
>> The difference you are seeing is probably due to the variance correction
>> applied in SPM and not applied in SPSS.
>>
>> Best Regards, Donald McLaren
>> =================
>> D.G. McLaren, Ph.D.
>> Research Fellow, Department of Neurology, Massachusetts General Hospital
>> and
>> Harvard Medical School
>> Postdoctoral Research Fellow, GRECC, Bedford VA
>> Website: http://www.martinos.org/~mclaren
>> Office: (773) 406-2464 <%28773%29%20406-2464>
>> =====================
>> This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED
>> HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is
>> intended only for the use of the individual or entity named above. If the
>> reader of the e-mail is not the intended recipient or the employee or
>> agent
>> responsible for delivering it to the intended recipient, you are hereby
>> notified that you are in possession of confidential and privileged
>> information. Any unauthorized use, disclosure, copying or the taking of
>> any
>> action in reliance on the contents of this information is strictly
>> prohibited and may be unlawful. If you have received this e-mail
>> unintentionally, please immediately notify the sender via telephone at
>> (773)
>> 406-2464 or email.
>>
>>
>> On Fri, Jul 5, 2013 at 3:37 AM, Susanne Dietrich <
>> [log in to unmask]> wrote:
>>
>>> I think classical and Bayesian statistics are available in SPM / Matlab.
>>> If averages are calculated with the Bayesian statistic, they differs
>>> slightly from the classical analysis which is, I think, only available in
>>> SPSS. Perhaps this could be the reason.
>>>
>>> Kind regards
>>> Susanne
>>>
>>>
>>> Am 04.07.2013 21:12, schrieb Mathalon, Daniel:
>>>
>>> Dear SPMers,
>>>>
>>>> We are running a full factorial model in SPM 8 using a condition
>>>> variable (representing two groups) and 8 additional covariates, including 1
>>>> Condition x Covariate interaction term.
>>>>
>>>> We've extracted the data from one voxel to compare results with SPSS
>>>> linear regression or ANOVA programs, fully expecting them to agree (as a
>>>> check that our script was working correctly).
>>>>
>>>> We find that the beta values estimated by SPM for that voxel for each
>>>> variable in the model and the regression coefficients estimated by SPSS are
>>>> in perfect agreement. The residual degrees of freedom in both SPM and SPSS
>>>> also agree.
>>>>
>>>> However, we are finding that the t-test values generated in SPM and
>>>> SPSS do not agree. For example, for the interaction term of interest, in
>>>> SPSS t =1.459, but in SPM's t-map t =1.3414. We can't figure out why, but
>>>> we suspect that the standard error of the beta must be estimated
>>>> differently (since the betas and the degrees of freedom appear to be the
>>>> same in SPSS and SPM).
>>>>
>>>> Does anyone have any ideas about why we are observing these
>>>> discrepancies? Any insights or strategies for tracking this down would be
>>>> greatly appreciated.
>>>>
>>>> Thanks,
>>>>
>>>> Dan
>>>>
>>>>
>>>> Daniel H. Mathalon, Ph.D., M.D.
>>>> Professor of Psychiatry
>>>> University of California, San Francisco
>>>>
>>>> Mail Address:
>>>> Psychiatry Service 116d
>>>> San Francisco VA Medical Center
>>>> 4150 Clement St.
>>>> San Francisco, CA 94121
>>>>
>>>> Office phone: (415) 221-4810, ext. 3860<%28415%29%20221-4810%2C%20ext.%203860>
>>>> Fax: (415) 750 6622 <%28415%29%20750%206622>
>>>> e-mail: [log in to unmask]
>>>>
>>>>
>>>
>>> --
>>> Dr. Susanne Dietrich
>>> Dept. of Neurology / MEG-Center
>>> University of Tübingen
>>> Otfried-Müller-Str. 47
>>> 72076 Tübingen
>>> phone: ++49 (0)7071-29 87708
>>> fax: ++49 (0)7071-29 5706
>>> email: [log in to unmask]
>>>
>>
>>
>>
>
> --
> San Francisco VA Medical Center 116D
> 4150 Clement St. Bld 8 Rm9B#29
> San Francisco CA 94121
> phone: (415) 221-4810x4335
> fax: (415) 750-6622
>
