[pymvpa] effect of signal on null distributions

[MS Al-Rawi]

Thanks to keep this topic up and for the nice reproduction of previous results. I guess changing the SNR could be considered as a multiplicative parameter, are you using one SNR value for all voxels? To get the full picture, how many permutations did you use? And how many samples per each run were there?


-Rawi



>________________________________
> From: J.A. Etzel <jetzel at artsci.wustl.edu>
>To: pkg-exppsy-pymvpa at lists.alioth.debian.org 
>Sent: Friday, February 15, 2013 3:02 PM
>Subject: Re: [pymvpa] effect of signal on null distributions
> 
>I posted images of some of the null distributions I get after changing the simulation code here: http://mvpa.blogspot.com/2013/02/comparing-null-distributions-changing_15.html - they now look a lot closer to Yaroslav and Michael's examples (though not exactly the same - I used different voxel counts, all voxels have the signal, etc.).
>
>So how we simulate the data really matters, more than I'd guessed.
>
>Do these conclusions seem sensible to all of you? Has anyone been able to take a look at the questions?
>
>thanks,
>Jo
>
>
>On 2/13/2013 5:06 PM, J.A. Etzel wrote:
>> Two questions:
>> Does my description seem accurate for normal_feature_dataset? In
>> particular, do you set the class B values as class A / snr, or generate
>> a separate set of normal values for A and B, then divide the B values?
>> 
>> What do the null distribution for different amounts of signal look like
>> if you try the simulations making the data as I describe (values from
>> two normal distributions with a small difference in mean but same
>> standard deviation)?
>
>-- Joset A. Etzel, Ph.D.
>Research Analyst
>Cognitive Control & Psychopathology Lab
>Washington University in St. Louis
>http://mvpa.blogspot.com/
>
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