I don't want to speak for the Freesurfer team, but I am 99% certain that it is 
Direct/Standard as you list them. There isn't any stepwise variable selection 
going on.

________________________________________
From: freesurfer-boun...@nmr.mgh.harvard.edu 
[freesurfer-boun...@nmr.mgh.harvard.edu] on behalf of L. Schweren 
[l.j.s.schwe...@umcg.nl]
Sent: Tuesday, March 25, 2014 6:08 AM
To: freesurfer@nmr.mgh.harvard.edu
Subject: Re: [Freesurfer] glmfit with correlated covariates

Dear FreeSurfer experts,

Thank you for your fast reply. We are extracting cortical thickness values for 
statistical sub-analyses in SPSS (due to corrections required with regard to 
family relatedness within the data); therefore we want to be a 100% certain 
that the statistical model we use in SPSS is identical to the model used by 
mri_glmfit. Can you confirm that, out of the multiple linear regression types 
listed below, mri_glmfit makes use of the Direct (or Standard) regression model?

Type

Characteristics

Direct (or Standard)

·         All IVs are entered simultaneously

Hierarchical

·         IVs are entered in steps, i.e., some before others
·         Interpret: R2 change, F change

Forward

·         The software enters IVs one by one until there are no more 
significant IVs to be entered

Backward

·         The software removes IVs one to one until there are no more 
non-significant IVs to removed

Stepwise

·         A combination of Forward and Backward MLR


Thank you again for your help,

Best wishes,
Lizanne






Date: Wed, 12 Mar 2014 11:04:00 -0400

From: Douglas Greve <gr...@nmr.mgh.harvard.edu>

Subject: Re: [Freesurfer] glmfit with correlated covariates

To: freesurfer@nmr.mgh.harvard.edu

Message-ID: <53207760.8010...@nmr.mgh.harvard.edu>

Content-Type: text/plain; charset="iso-8859-1"





It is just a standard multiple regression analysis where all regressors

are fit simultaneously. One weight does not include that of another weight.

doug





On 3/12/14 5:16 AM, L. Schweren wrote:

>

> Dear FreeSurfer experts,

>

> I am attempting to disentangle the effects of different features of

> pharmacological treatment on cortical thickness.

>

> I am running glmfit from the commandline, with multiple covariates

> (a.o. Z_Age, Z_TreatmentDuration and Z_StartAge) in the fsgd-file.

> These covariates are correlated up to approximately  0.6 , which to my

> understanding is not ideal yet not inducing collinearity. Running

> glmfit, I do not get any errors such as ill-designed matrix or so.

>

> My question regards the way the different regression weights are

> calculated in each voxel. If I test the variance in CT of voxel A

> explained by for example TreatmentDuration, and part of the variance

> in voxel A is explained by both TreatmentDuration and StartAge, will

> the regression weigth of TreatmentDuration than include the part that

> is also explained by StartAge? Or are all other covariates first

> "regressed out" of the variance, such that the variable I test can

> only explain the variance that was not explained by any of the other

> covariates?

>

> Thank you very much, your help is very much appreciated!

>

> Best wishes,

>

> Lizanne

>

>

>

> _______________________________________________

> Freesurfer mailing list

> Freesurfer@nmr.mgh.harvard.edu

> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer



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