[pymvpa] Accuracy for Each Timepoint in Event-Related Analysis

[Daniel P. Bliss]

Thanks!


On Sat, Jan 11, 2014 at 6:20 AM, Nick Oosterhof <nikolaas.oosterhof at unitn.it
> wrote:

>
> On Jan 10, 2014, at 8:15 PM, Daniel P. Bliss wrote:
>
> > I think this question was based on a misinterpretation of what the
> event-related analysis actually does.  I was assuming that it conducted a
> separate classification analysis for each timepoint, but now I'm inferring
> that it conducts a single analysis, treating the timepoints as independent
> features.  Is that true?
>
> Not necessarily independent, but yes, data from different timepoints is
> used in the analysis.
>
> > Is there a way to automate separate analyses for different timepoints in
> PyMVPA?
>
> Yes, but it depends a bit on what type analysis you want to do.
> I assume you have a dataset ds with trials that have N timepoints each.
> Add a sample attribute, say 'time_in_trial', and assign to each sample a
> value from 1 to N. Then do
>
> ds_splits=split_by_sample_attribute(ds, 'time_in_trial')
>
> and run your analysis separately for each dataset in ds_splits, e.g.
>
> ds_results=[]
> for ds_split in ds_splits:
>    # your code here to get ds_result
>   ds_results.append(ds_result)
>
> result=hstack(ds_results)
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