[Mne_analysis] Comparison of brain responses under three different conditions?

Denis-Alexander Engemann denis.engemann at gmail.com
Sun Dec 21 09:49:40 EST 2014
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Should be Greenhouse-Geisser,
also see source code:

https://github.com/mne-tools/mne-python/blob/master/mne/stats/parametric.py#L249

2014-12-21 15:47 GMT+01:00 Denis-Alexander Engemann <
denis.engemann at gmail.com>:

> [f,p]=f_twoway_rm(data,factor_levels=[2,3], correction=True)
>
>
> 2014-12-21 15:45 GMT+01:00 Maria Hakonen <maria.hakonen at gmail.com>:
>
>> Hi Denis,
>>
>> Are you sure that the sphericity correction is implemented by f_twoway_rm?
>> I tested it with data where the assumption of sphericity is violated. I
>> calculated p values with Statistica and corrected them using the
>> lower-bound estimate, Greenhouse-Geisser correction and the Huynh-Feldt
>> correction. However, the p-values given by f_twoway_rm were equal with the
>> uncorrected values I calculated with Statistica. I used f_twoway_rm as:
>>
>> [f,p]=f_twoway_rm(data,factor_levels=[2,3])
>>
>> where data is hemisphere*sesion ANOVA table.
>>
>> -Maria
>>
>> 2014-12-21 15:08 GMT+02:00 Denis-Alexander Engemann <
>> denis.engemann at gmail.com>:
>>
>>>
>>>
>>> 2014-12-21 14:00 GMT+01:00 Maria Hakonen <maria.hakonen at gmail.com>:
>>>
>>>> Hi,
>>>>
>>>> Is it that when I have hemisphere*session ANOVAs, I can use
>>>> f_twoway_rm, and this gives the corrected values?
>>>>
>>>
>>> with correction=True
>>>
>>>
>>>> Should one-way ANOVA be used in the cases where I am not interested in
>>>> hemisperic asymmetry? Is there any function for one way ANOVA that gives
>>>> sphericity corrected values?
>>>>
>>>>
>>> A one-way ANOVA is currently not implemented. But you can just look at
>>> the relevant main effect. Should be equivalent.
>>>
>>>
>>>> -Maria
>>>>
>>>> 2014-12-21 14:21 GMT+02:00 Denis A. Engemann <denis.engemann at gmail.com>
>>>> :
>>>>
>>>>> Hi Maria,
>>>>>
>>>>> On 21 Dec 2014, at 12:51, Maria Hakonen <maria.hakonen at gmail.com>
>>>>> wrote:
>>>>>
>>>>> Hi Denis,
>>>>>
>>>>> Should I use two-way ANOVA if I also want to compare hemispheres, i.e.
>>>>> have a 2*3 (hemisphere*session) ANOVA table?
>>>>>
>>>>>
>>>>> Sounds like the repeated measures anova is what you want to do here
>>>>> indeed. I just realized the sphericity correction is actually implemented
>>>>> by our code. So that should fly. Checkout the correction parameter.
>>>>>
>>>>>
>>>>> I would need to do group level analysis.
>>>>>
>>>>> -Maria
>>>>>
>>>>>
>>>>> 2014-12-21 12:38 GMT+02:00 Denis-Alexander Engemann <
>>>>> denis.engemann at gmail.com>:
>>>>>
>>>>>> Hi Maria,
>>>>>>
>>>>>> 2014-12-21 10:18 GMT+01:00 Maria Hakonen <maria.hakonen at gmail.com>:
>>>>>>
>>>>>>> Hi all,
>>>>>>>
>>>>>>> I would need to find the time intervals where there are
>>>>>>> statistically significant differences between MEG responses measured under
>>>>>>> three different conditions.  I study the responses by comparing the mean
>>>>>>> values in steps of 200 ms between the range of 400-2400 ms. I would like to
>>>>>>> do this for responses from several brain areas. What could be the best way
>>>>>>> to compare the mean values of the tree responses?
>>>>>>>
>>>>>>>
>>>>>> Single subject or group level?
>>>>>> For single subject a standard F-test would be appropriate. For groups
>>>>>> a oneway repeated measures ANOVA, if I understand your design correctly
>>>>>> (simply 3 different conditions).
>>>>>>
>>>>>>
>>>>>>> I have planned to try f_twoway_rm in a loop. However, when rmANOVA
>>>>>>> is used, sphericity test is needed, and if the assumption of sphericity is
>>>>>>> violated, p values need to be corrected. Is there any function for this in
>>>>>>> Python (e.g. Mauchly's sphericity test and Greenhouse-Geisser test)?
>>>>>>>
>>>>>>>
>>>>>> Note that this only needed if you have more than 2 factors with more
>>>>>> than two levels. Currently this is not implemented in our stats code. The
>>>>>> other questions is why you need a 2-way test for 3 conditions. I probably
>>>>>> did not fully understand your design.
>>>>>>
>>>>>> Denis
>>>>>>
>>>>>>
>>>>>>> Many thanks already in advance!
>>>>>>>
>>>>>>> Regards,
>>>>>>> Maria
>>>>>>>
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