JiscMail Logo
Email discussion lists for the UK Education and Research communities

Help for FSL Archives


FSL Archives

FSL Archives


FSL@JISCMAIL.AC.UK


View:

Message:

[

First

|

Previous

|

Next

|

Last

]

By Topic:

[

First

|

Previous

|

Next

|

Last

]

By Author:

[

First

|

Previous

|

Next

|

Last

]

Font:

Proportional Font

LISTSERV Archives

LISTSERV Archives

FSL Home

FSL Home

FSL  May 2012

FSL May 2012

Options

Subscribe or Unsubscribe

Subscribe or Unsubscribe

Log In

Log In

Get Password

Get Password

Subject:

TBSS: how to compare subsets?

From:

Joerg Trojan <[log in to unmask]>

Reply-To:

FSL - FMRIB's Software Library <[log in to unmask]>

Date:

Mon, 7 May 2012 09:44:13 +0100

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (17 lines)

Dear TBSS experts,

let's say I have two factors, AB (with levels A and B) and XY (with levels X and Y). Everything is fine if I only want to compare A with B or X with Y or if I use the full two-factor design: I simply copy all FA datasets in a directory, run the TBSS scripts and then apply randomise with an appropriate design.mat.

However, what if I want to compare subsets of these data only and omit the rest? For example, I have a sample of patients and matched healthy controls. The patients differ in respect to a particular feature, and I'd like to compare these two subgroups. In more abstract terms, I only want to compare levels A and B for those datasets in which the level of XY is X and not Y.

As far as I can tell, there's no way to tell randomise to omit cases, because it expects a design.mat of the length of all datasets present in the given directory. Or did I miss something in the documentation? 

So what I have to do is create a new directory, copy only those FA datasets which should be included in the particular analysis (that is, those in which the level of XY is X and not Y) to this directory, run the TBSS scripts there again, and apply randomise with another design.mat constructed for this purpose.

I have two concerns regarding this approach:
1. It can become a bit tedious, especially if there are more factors or factor levels and thus more design cells for which separate comparisons are potentially to be conducted. For each of these, a separate directory and a fresh run of the TBSS scripts is needed. But I guess this can't be helped?
2. All of the individual analyses will be based on separate subsets of the data. This also means that the mean skeleton will differ, making it harder to compare the results from the different analyses. So I had the idea of using the skeleton from the full-sample TBSS in the randomise runs for the individual subset comparisons. In your opinion, would this be reasonable and methodologically sound?


Thanks in advance,
Jörg

Top of Message | Previous Page | Permalink

JISCMail Tools


RSS Feeds and Sharing


Advanced Options


Archives

April 2014
March 2014
February 2014
January 2014
December 2013
November 2013
October 2013
September 2013
August 2013
July 2013
June 2013
May 2013
April 2013
March 2013
February 2013
January 2013
December 2012
November 2012
October 2012
September 2012
August 2012
July 2012
June 2012
May 2012
April 2012
March 2012
February 2012
January 2012
December 2011
November 2011
October 2011
September 2011
August 2011
July 2011
June 2011
May 2011
April 2011
March 2011
February 2011
January 2011
December 2010
November 2010
October 2010
September 2010
August 2010
July 2010
June 2010
May 2010
April 2010
March 2010
February 2010
January 2010
December 2009
November 2009
October 2009
September 2009
August 2009
July 2009
June 2009
May 2009
April 2009
March 2009
February 2009
January 2009
December 2008
November 2008
October 2008
September 2008
August 2008
July 2008
June 2008
May 2008
April 2008
March 2008
February 2008
January 2008
December 2007
November 2007
October 2007
September 2007
August 2007
July 2007
June 2007
May 2007
April 2007
March 2007
February 2007
January 2007
2006
2005
2004
2003
2002
2001


WWW.JISCMAIL.AC.UK

Secured by F-Secure Anti-Virus CataList Email List Search Powered by the LISTSERV Email List Manager