Hi Mark,
To make this concrete, could you perhaps lay out an example of the type
of model that you're thinking of?
thanks,
-MH
On Mon, 2011-02-14 at 19:11 +0000, Mark Jenkinson wrote:
> Hi Michael,
>
> What you say is true if this is the *only* contrast.
> However, it is common to also have contrasts on
> the individual group responses, in which case it
> does make a difference. There are two schools
> of thought on what the "best" option is, but they
> are not totally equivalent - only in certain aspects.
>
> All the best,
> Mark
>
>
>
> On 14 Feb 2011, at 18:29, Michael Harms wrote:
>
> > Hi Jesper,
> > Just to elaborate, in the model that I laid out as an example, you will
> > indeed get different inference on the intercept regressor if there is a
> > mean component to other regressors, but in that model, the intercept
> > regressor was simply modeling the mean, and was not itself a regressor
> > of interest. In the more typical neuroimaging case involving groups
> > (say 2 groups, but no explicit column of one's) with gender as a
> > covariate, then inference on the CONTRAST of the two groups is also
> > identical regardless of how you model gender. That is, you get
> > identical inference on the contrast of the group betas, and identical
> > inference on gender as well, using any gender2 EV of the form: gender2 =
> > a*gender + b. That is why I originally wrote that there is no need to
> > demean a term such as gender.
> >
> > cheers,
> > -MH
> >
> > On Mon, 2011-02-14 at 14:37 +0000, Jesper Andersson wrote:
> >> Dear Michael,
> >>
> >>> Sorry Mark, but I don't follow you yet. Whether you subtract the mean
> >>> will certainly affect the beta estimates, but it should have no
> >>> influence on the resulting inference. That is, say I have a GLM
> >>> with an
> >>> intercept column (all one's), and then choose to model a main effect
> >>> of
> >>> gender using a column containing 1's for males, and 0 for females.
> >>> Under that model, the resulting beta is straightforwardly
> >>> interpreted as
> >>> the additional amount added for males. Now, that gender column will
> >>> have a non-zero mean. But as regards the inference on whether the
> >>> gender term is significant, I will get the exact same p-values
> >>> regardless of whether I demean the gender column or just use the
> >>> original.
> >>
> >> you are right that you will get the same inference for the gender
> >> regressor if you demean it or not. BUT you will get different
> >> inference for the mean regressor (assuming we are modeling the overall
> >> mean) depending on if you demean the gender regressor or not. In GLM
> >> any variance that is shared by more than one regressor will not be
> >> included as part of the inference on either of those regressors. It
> >> would only get included if you performed an F-test spanning all
> >> regressors that have a share in that variance.
> >>
> >> Hence, it does indeed make a difference if you demean or not. It is
> >> not always obvious what is the "correct" thing to do. The conservative
> >> option is typically to not demean.
> >>
> >> Does that clarify things?
> >>
> >> Jesper
> >>
> >>
> >>> For that matter, I'll get the exact same inference if I
> >>> multiply the gender column by any constant.
> >>>
> >>> cheers,
> >>> -MH
> >>>
> >>> On Mon, 2011-02-14 at 10:22 +0000, Mark Jenkinson wrote:
> >>>> Dear Michael,
> >>>>
> >>>> I'm afraid this is not correct.
> >>>>
> >>>> It may be the case for other types of statistical test, but not for
> >>>> the GLM.
> >>>> In the GLM (as typically implemented in FSL and other neuroimaging
> >>>> packages) there is no distinction between continuous and discrete
> >>>> variables.
> >>>> Everything is treated as a regressor and you are doing multiple
> >>>> regression.
> >>>> The consequence of this is that if two regressors each contain a
> >>>> non-zero
> >>>> mean, then any true non-zero mean in the data will tend to be split
> >>>> across
> >>>> these regressors (especially as the mean is often a strong
> >>>> signal). So it
> >>>> makes a big difference to the estimated parameters (the coefficients
> >>>> associated with the regressors) whether you remove the mean from one
> >>>> of them or not. It is true that if you span the same space
> >>>> (assuming that
> >>>> some set of regressors adds up to a flat mean). However, it is the
> >>>> fact
> >>>> that the mean signal will get shared between the regressors which
> >>>> causes a problem and *will* have an effect on the parameters
> >>>> associated
> >>>> with the "mean" regressors, which is normally what is of interest and
> >>>> hence a big issue.
> >>>>
> >>>> All the best,
> >>>> Mark
> >>>>
> >>>>
> >>>>
> >>>> On 11 Feb 2011, at 18:02, Michael Harms wrote:
> >>>>
> >>>>> Hi Gwenaelle,
> >>>>> Why does gender need to be demeaned? You should get identical
> >>>>> results
> >>>>> either way because the intercept and gender terms together model the
> >>>>> same space, regardless of whether gender is demeaned. Demeaning
> >>>>> really
> >>>>> only matters when trying to interpret a main effect when that
> >>>>> effect is
> >>>>> also included as part of an interaction term with a continuous
> >>>>> variable.
> >>>>>
> >>>>> cheers,
> >>>>> -MH
> >>>>>
> >>>>>
> >>>>> On Fri, 2011-02-11 at 17:52 +0000, Gwenaëlle DOUAUD wrote:
> >>>>>> Hi,
> >>>>>>
> >>>>>> gender needs to be demeaned. It is not necessary to split the age
> >>>>>> per group, unless you expect an interaction of age with group...
> >>>>>>
> >>>>>> Cheers,
> >>>>>> Gwenaelle
> >>>>>>
> >>>>>>
> >>>>>>> De: Stijn Michielse <[log in to unmask]>
> >>>>>>> Objet: [FSL] 3 groups randomise
> >>>>>>> À: [log in to unmask]
> >>>>>>> Date: Vendredi 11 février 2011, 14h57
> >>>>>>> Dear FSL Experts,
> >>>>>>>
> >>>>>>> The project I'm working on has 258 subjects in the
> >>>>>>> population divided over 3 groups. Processing in TBSS is
> >>>>>>> straightforward and I have some questions regarding the
> >>>>>>> randomise tool.
> >>>>>>>
> >>>>>>> Using the randomise tool, I first started creating the
> >>>>>>> design matrix and contrast matrix (named design.mat and
> >>>>>>> design.con). For performing a simple T-test everything is
> >>>>>>> straightforward with contrasts 1 and -1 for corresponding
> >>>>>>> groups. But things get complicated with the introduction of
> >>>>>>> confounders. Our groups are not matched since we would like
> >>>>>>> to include as many individuals as possible. Now we would
> >>>>>>> like to add age, gender and handedness as a confounder in
> >>>>>>> the model.
> >>>>>>>
> >>>>>>> Checking the JISCMail FSL Archives clue's regarding the
> >>>>>>> demeaning of confounders pop up. Demeaning per group is
> >>>>>>> necessary since our groups are not matched. Gender is a
> >>>>>>> bi-directional (being either female or male) variable and
> >>>>>>> doesn't need to get demeaned. In our case we have demeaned
> >>>>>>> handedness since we apply an Oldfield scale (-100 is fully
> >>>>>>> left-handed, +100 is fully right-handed, with value's in
> >>>>>>> between). To know sure we do the right thing in analysing, I
> >>>>>>> attached our design matrix and contrast matrix. In the
> >>>>>>> design matrix the first column is group 1, second column is
> >>>>>>> group 2 and the third column is group 3. As you might
> >>>>>>> notice, row 48 has a group change since this individual is
> >>>>>>> classified as patient after TBSS processing (some more
> >>>>>>> changes are seen further on).
> >>>>>>> For investigating the influence of confounders I added
> >>>>>>> three extra columns; column 4 for age (demeanded per group),
> >>>>>>> column 5 for gender (not demeaned) and column 6 for
> >>>>>>> handedness (demeaned per group). Is it necessary to add
> >>>>>>> specified columns per group for age, padding the other
> >>>>>>> groups with 0? Later we may add more confounders if it
> >>>>>>> survives.
> >>>>>>>
> >>>>>>> Executing the randomise tool with the two designs goes like
> >>>>>>> this:
> >>>>>>> randomise -i all_FA_skeletonised -o tbss -m
> >>>>>>> mean_FA_skeleton_mask -d design.mat -t design.con -n 5000
> >>>>>>> --T2 -V
> >>>>>>>
> >>>>>>> Can someone please review the attached design matrix and
> >>>>>>> contrast matrix and give some advice?
> >>>>>>>
> >>>>>>>
> >>>>>>> Kind regards,
> >>>>>>>
> >>>>>>> Stijn Michielse
> >>>>>>> Research Assistant
> >>>>>>> Dept. Psychiatry and Neuropsychology
> >>>>>>> Maastricht University
> >>>>>>> E-mail: [log in to unmask]
> >>>>>>
> >>>>>> --------------------------------------------------------------------
> >>>>>>
> >>>>>> Gwenaëlle Douaud, PhD
> >>>>>>
> >>>>>> FMRIB Centre, University of Oxford
> >>>>>> John Radcliffe Hospital, Headington OX3 9DU Oxford UK
> >>>>>>
> >>>>>> Tel: +44 (0) 1865 222 523 Fax: +44 (0) 1865 222 717
> >>>>>>
> >>>>>> www.fmrib.ox.ac.uk/~douaud
> >>>>>>
> >>>>>> --------------------------------------------------------------------
> >>>>>>
> >>>>>>
> >>>>>>
> >>>>>
> >>>
> >
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