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

Sun Dec 21 10:49:09 EST 2014

Hi Maria,

2014-12-21 16:35 GMT+01:00 Maria Hakonen <maria.hakonen at gmail.com>:

> Hi Denis,
>
> I tried that and got  the following p values: 0.02993846,  0.47414992
> The corresponding Greenhouse-Geisser corrected values computed with
> Statistica were: 0.026775, 0.462423.
> Huynh-Feldt correction gives 0.023894, 0.473539 and lower-bound
> correction 0.036458, 0.427778
>
> The uncorrected values calculated with Statistica and Python are equal but
> also the corrected values are almost equal. So, maybe f_twoway_rm works
> correctly now?
>
>
There might be some minor implementation-related difference, would be good
to investigate.
Generally the ANOVA function is tested against R and SPSS, event for the
correction:

https://github.com/mne-tools/mne-python/blob/master/mne/stats/tests/test_parametric.py#L10

I would therefore trust the results.

> It would also be interesting to do post-hoc comparisons between conditions
> but I think that Python doesn't have function for post-hoc test, or at
> least I didn't found it?
>
>
What do you mean? You simply compute paired t-tests and correct the alpha.

-Denis

> -Maria
>
> 2014-12-21 16:49 GMT+02:00 Denis-Alexander Engemann <
> denis.engemann at gmail.com>:
>
>> 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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