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

Maria Hakonen maria.hakonen at gmail.com
Sun Dec 21 11:26:06 EST 2014
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Hi Denis,

I have used Newman-Keuls test in Statistica, but maybe paired t-test would
work as well if I correct the alpha.
Did you mean that I could simply use stats.ttest_rel and then correct the
values with, for example, fdr_correction?

-Maria

2014-12-21 17:49 GMT+02:00 Denis-Alexander Engemann <
denis.engemann at gmail.com>:

> 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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