Hi,
I have another question related to empty EVs. I'm working on a single trial
study with multiple runs and analyses based on subject responses.
Sometimes there are missing EVs because a subject did not make a certain
type of response. This is not a problem, since as suggested below I then
leave out the empty Evs and leave the contrast order the same. I put zeros
for the non-existent contrasts (due to the missing EV) and correct the
contrasts to account for having fewer EVs. This appears to generate empty
(0) copes, and I then combine only the non-empty copes in a 2nd level
analysis to create 1 cope per subject to be included in a 3rd level
analysis.
However, I'm running into a problem when a contrast only exists in 1 run
(you can't do a 2nd level, since the dof would be 0). Should I in this case
just register the cope from one individual run to standard space in the
first level analysis such that I can include it into a 3rd level analysis or
does this somehow mess up the dofs?
I'll try a couple of things, but any feedback would be appreciated.
Best, Theo
on 6/5/04 12:17 PM, Mark Jenkinson at [log in to unmask] wrote:
> Hi,
>
> This depends on what contrasts you are trying to use.
> If you have a contrast that only contains "missing" EVs then
> you can't have this if you drop these EVs. However, I'm
> assuming that the contrasts of interest for the group analysis
> are not going to include only missing EVs for any subject or
> session. Otherwise there is no information that this subject
> or session can contribute to the group analysis in this case
> and just shouldn't be included for the higher level analysis.
>
> If you do make contrasts to see certain effects in the lower
> level analysis that you don't pass up to the group analysis
> (e.g. a 0 0 1 0 0 ... 0 type contrast for each EV) then it doesn't
> matter what you put in this place for the lower level analysis
> as far as the group analysis is concerned.
>
> The best approach for you is probably to restrict yourself to
> first level analyses that only contain the contrast of interest
> for the group analysis. This should include at least one
> non-missing EV for each session/subject (if not, exclude
> that session/subject from the group analysis). Once you've
> done this the group analysis is straightforward.
>
> If you are interested in looking at other contrasts in the lower
> level, you can always run different contrasts again under
> post-stats to have a look (which is quick and easy).
>
> All the best,
> Mark
>
>
> On Thursday, June 3, 2004, at 10:42 pm, Sam Harris wrote:
>
>> Hi, Mark
>>
>> Thanks for your response. It seems to me, however, that I'm not out of
>> the woods yet--because once I drop the missing EVs for any given run,
>> that run will then not have the same number of contrasts.
>> Have I misunderstood you somewhere?
>>
>> Best,
>> Sam
>>
>> On Jun 3, 2004, at 11:15 AM, Mark Jenkinson wrote:
>>
>>> Hi,
>>>
>>> What you really need to do is create consistent *contrasts* not EVs.
>>> It is contrasts that get fed up into the higher level analyses, not
>>> EVs.
>>> If an EV is missing, then do not have it in the model. However, when
>>> formulating your contrasts make sure that the numbering of the
>>> contrasts
>>> is the same in each subject/session so that these feed up
>>> consistently.
>>>
>>> You'll have to be careful to select the appropriate EVs each time, as
>>> their numbering will change whenever some are missing.
>>> For example, if you had a contrast like 1 0 -1 0 0 1 0 and in one case
>>> EV2 was missing then the design matrix would only have 6 columns,
>>> not 7, and the contrast would become 1 -1 0 0 1 0 in this
>>> instance (assuming the other EVs were present).
>>>
>>> Obviously these missing EVs cannot contribute to the contrast, but
>>> as long as each contrast contains at least one EV then you are fine.
>>>
>>> All the best,
>>> Mark
>>>
>>>
>>> Sam Harris wrote:
>>>
>>>> I'm attempting to analyze event-related data acquired on 14 subjects,
>>>> 3 runs each, in which
>>>> certain EVs, in any given run, were not represented. For example,
>>>> subject #6, in run #2, may not
>>>> have provided a single example of EV 8 (while all other subjects did,
>>>> as did subject #6 himself in
>>>> runs 1 and 3). I'm wondering if there is any way of creating a
>>>> "place-holder" model (in three-
>>>> column format), so that all subjects and all runs can contain the
>>>> same number of EVs, thereby
>>>> allowing analysis at the group level. Could I, in the above case,
>>>> create a spurious model for EV 8
>>>> (in subject #6, run #2), with a fictional onset time and duration,
>>>> and scale it at (or near) 0? Or
>>>> could I create an extra volume, tacked onto to the end of all scans,
>>>> that represented the average
>>>> value of each functional run, and then reference this timepoint in my
>>>> model as a dummy-EV?
>>>> Needless to say, I'm looking for a solution that will produce, in the
>>>> worst case, a type 2 error.
>>>>
>>>> Thanks for your help.
>>>> Sam
>>>>
>>>>
>>>
>
_____________________________________________________________
Theo van Erp
Lab Manager, PhD Candidate
Cannon Lab
Department of Psychology [log in to unmask]
University of California Los Angeles voice (310) 794-9673
1285 Franz Hall, room 5556 fax (310) 794-9740
Los Angeles, California, 90095-1563
http://www.bol.ucla.edu/~vanerp
_____________________________________________________________
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