2-Way Repeated Measures ANOVA + contrasts about mne-python HOT 17 CLOSED

++++++1 :)

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... won't be 0.5. however ;-)

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I have a basic implementation of ANOVA capability for balanced and completely specified models at https://github.com/christianmbrodbeck/Eelbrain/blob/master/eelbrain/analyze/glm.py . Model specification is done with the factors I described in Issue #133, but that could be modified.

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To be more specific:

• for univariate dependent variables, I have implemented incremental f-tests for fixed effects models which can include scalar and categorial predictors example.
• For balanced models I have also implemented random effects models example.
• For the latter method I have added a class that can fit the model to several data points simultaneously, which can then be used for a cluster permutation test example. Here the ndvar object is useful because you can submit data with an arbitrary dimensions (time, sensor, source, ....) and get the result map of F- or p-values (or cluster extent) back with the same dimensions (minus the 'case' dimension).

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Hi chris,

thanks! let's get back and see how we can interface / support integrate your efforts as the anova issue will transform into a WIP PR. Maybe we can setup that up together. From my side however not before 0.5

Best,
Denis

On 06.12.2012, at 00:50, Christian Brodbeck [email protected] wrote:

I have a basic implementation of ANOVA capability for balanced and completely specified models at https://github.com/christianmbrodbeck/Eelbrain/blob/master/eelbrain/analyze/glm.py . Model specification is done with the factors I described in Issue #133, but that could be modified.

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Addressed by #580. Closing.

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hey there, so does mne-python have a repeated measures anova? if so what's the name of the function - and are there any docs?

I'd also be willing to provide a more general package atom (ebuild) for my distribution (gentoo) if I can use your software.

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hi,

look at this diff:

https://github.com/mne-tools/mne-python/pull/580/files

you'll see the example and the function of interest to you is : f_twoway_rm

the rendered example is at :

http://martinos.org/mne/auto_examples/stats/plot_cluster_stats_time_frequency_repeated_measures_anova.html

if some doc or function is missing don't hesitate to send us a pull request to help us making MNE-Python better.

Regarding gentoo packaging that would be great. Maybe @mluessi can give a hand too as a gentoo user.

Hope this helps,
Alex

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Hi @TheChymera , now @agramfort was faster. Two things to add. First, this is optimized for mass-univariate use cases (estimate thousands of models at once), the interface is hence more Matlab-like, matrix oriented an allows for passing large matrices with thousands of observations per person. Functionality is tested against R and SPSS and of course you can also pass behavioral data with one observation per subject. Second currently we only have 2-way repeated measures ANOVA. Extending the code is relatively easy though, e.g. supporting more factors, non-repeated factors (e.g. groups) and covariates. However I did not have a real use case for this since simple 2-way ANOVA models are already quite complex in an MEG context. From my point of view adding groups support, a third factor and a covariate would make sense. From your post records across different projects (Psychopy, statsmodels) it seems you're looking for standard functionality / sth. for behavioural data. Why not R then? Gentoo packaging is of course a nice idea ;-) and of course if you're interested in extending our ANOVA support contributions are always welcome.

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@dengemann Well, I want to keep all of my data post-processing and analysis in python instead of switching between programs. I tried Rpy but for some reason I have issues formatting my pandas dataframe for R...

Anyway, do you have a one-way repeated measure ANOVA? or cam I use f_twoway_rm for that as well? I have 5 conditions presented to 10 participants with 100 trials each - I'm only interested in the variance of the conditions, not of the participants etc.

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@TheChymera

Here is some pseudo code / snippet thatdemonstrates how I use Rpy (for 'small' data / when it comes to standard analyses I always do this at some point --- I guess Python will never, at least not in the next 5-10 years catch up with all the general stats provided in R -- it's strength is in particular and advanced packages. Python makes tackling difficult / new problems fun, so many developers / scientists choose it to start a new project rather than reimplementing what's e.g. in R. People simply want to work on new stuff ;-). I can see that Rpy does not come with a self-evident API and you need to know both R and Python to take full advantage of it. But given my prediction it's worth the effort, and actually it works nicely.)

import pandas.rpy.common as com
import pandas as pd
base = com.importr('base')
stats = com.importr('stats')

# .... read data and stuff, a few lines omitted
aggfunc = np.mean  # how to aggregegate
agglist = ['subject', 'A', 'B', 'C']  # splitby 
fml = 'latency ~ A * B * C + Error(subject/ (A * B * C))'  #  formula string
df_ = df.groupby(agglist).agg(aggfunc).reset_index()  # final structure to be tested
dfr = com.convert_to_r_dataframe(df_, True)  # convert from pandas to R and make string columns factors
fml_ = Formula(fml)  #  make a formula    obect
print str(base.summary(stats.aov(fml_, dfr)))

As to the one-way in mne-python.... I don't know whether the implemented version accepts 1-factor only input, I never tried. You would need to setup a matrix with 5 columns (one for each level) and try to pass [5] as factor_levels argument.

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Btw. you can also take a look at Roger Lew's pyvttbl package https://pypi.python.org/pypi/pyvttbl/0.4.1.0, I think this the most comprehensive collection of ANOVA models in Python. I drew from there and we actually had discussions at statsmodels to refactor his code and add it there. But no one including himself or myself had time for it.

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@TheChymera having mne-python in the gentoo science overlay would be perfect. I will be happy to assist with making an ebuild :).

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@mluessi I'm caught up quite a bit in my master thesis until the start of december, so since I won't be using the mne-python ANOVA function, I'll only get to the ebuild after that. There are a number of high-profile neuroscience packages still not merged into gentoo-sci - there's a list with them (as well as some info on neuro~gentoo) in this neurogentoo guide. If you have any provisorial ebuild for mne-python I can add it to the listing, maybe someone will notice it even before I get more time.

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@TheChymera btw I took a look at running one-way repeated measures with our ANOVA. The contrast coding as is implemented right now prevents such a model, adding support for it should not be too difficult though. I just won't have time for it, unless an urgent use case emerges on my end which I don't see coming.

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@TheChymera thanks for the link. I will look into making an mne-python ebuild, it should be terribly simple, unlike e.g. packing for Debian ;). Good luck with your thesis.

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+1

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