[pymvpa] design matrix identical across sessions
debian at onerussian.com
Tue May 10 00:50:41 UTC 2016
On Mon, 09 May 2016, Wolfgang Pauli wrote:
> I am trying to perform an mvpa analysis of an experiment in which I have 16
> different trial types and 4 sessions, 4 trial types in each session (run).
> Based on the tutorial, I was getting started by using fit_event_hrf_model,
> like so:
> evds = fit_event_hrf_model(ds, events, time_attr='time_coords',
> condition_attr=('onset'), return_model=True)
> where ds is an openfmri dataset (get_model_bold_dataset).
> I was trying to figure out why I would always get the warning that the
> design matrix was singular, and eventually ended up investigating the
> design matrix of the model.
> I used matplotlib to plot the design matrix, split it up into the four
> session, and found that the four parts were IDENTICAL. What could I be
> doing wrong? Obviously, it shouldn't be the same, because there are
> different trial types in the four sessions, and the trial order is also
> Furthermore, the design matrix has 26 regressors. I don't quite understand
> where that number is coming from, as I have 16 unique event types, and 4
most probably that events definition was was the same in each of the
chunks... but also note that if you do it in a dataset which has
multiple chunks, you want to have condition_attr=['onset', 'chunks'] if
you want to make a model per each onset x chunk pair as was demoed e.g.
if you share your data, and ideally your investigation script then we
could may be look deeper
Yaroslav O. Halchenko
Center for Open Neuroscience http://centerforopenneuroscience.org
Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419
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