[Mne_analysis] mne.stats.linear_regression

[Emma Chen]

Thanks very much, Alexandre & Alik!
It does work with only three dummy variables.

Best,
Emma

On Fri, Dec 2, 2016 at 4:48 PM, Alik Widge <alik.widge at gmail.com> wrote:

> Indeed, this has a trivial solution: drop the column corresponding to your
> "base" condition, which will then load on the intercept.
>
> On Dec 2, 2016 5:49 AM, "Alexandre Gramfort" <alexandre.gramfort at telecom-
> paristech.fr> wrote:
>
>> hi Emma,
>>
>> yes in your case the intercept column is the sum of the dummy variables
>> so your design matrix is ill-posed.
>>
>> it's a standard GLM problem. Cf. articles and textbooks
>>
>> Maybe someone has a good suggestion for you.
>>
>> Alex
>>
>>
>> On Fri, Dec 2, 2016 at 11:03 AM, Emma Chen <emma.chen.w at nyu.edu> wrote:
>>
>>> Hi Mne users,
>>>
>>> Is there a way to use mne.stats.linear_regression on epoch data or stc
>>> data  with *binary/categorical* predictors?
>>>
>>> In the MEG data I'm analyzing, the  regressor I would like to use is a
>>> categorical variable with 4 different categories of objects indicating
>>> which type of object participants saw in each trial.
>>>
>>> I've tried to create the design_matrix with a column of intercept + 4
>>> column of dummy variables for each category. But it didn't work. Following
>>> is the error messages I got:
>>>
>>> ------------------------------------------------------------
>>> ---------------
>>>
>>> LinAlgError                               Traceback (most recent call
>>> last)
>>> <ipython-input-49-728b72ea06ce> in <module>()
>>> ----> 1 res=mne.stats.linear_regression(epochs_ALL, design_matrix,
>>> names)
>>> /Users/wc47/anaconda/envs/mne-python/lib/python2.7/site-packages/mne/stats/regression.pyc
>>> in linear_regression(inst, design_matrix, names)
>>>      85     logger.info(msg + ', (%s targets, %s regressors)' %
>>>      86                 (np.product(data.shape[1:]), len(names)))
>>> ---> 87     lm_params = _fit_lm(data, design_matrix, names)
>>>      88     lm = namedtuple('lm', 'beta stderr t_val p_val mlog10_p_val')
>>>      89     lm_fits = {}
>>>
>>> /Users/wc47/anaconda/envs/mne-python/lib/python2.7/site-packages/mne/stats/regression.pyc
>>> in _fit_lm(data, design_matrix, names)
>>>     125     df = n_rows - n_predictors
>>>     126     sqrt_noise_var = np.sqrt(resid_sum_squares /
>>> df).reshape(data.shape[1:])
>>> --> 127     design_invcov = linalg.inv(np.dot(design_matrix.T,
>>> design_matrix))
>>>     128     unscaled_stderrs = np.sqrt(np.diag(design_invcov))
>>>     129     tiny = np.finfo(np.float64).tiny
>>>
>>> /Users/wc47/anaconda/envs/mne-python/lib/python2.7/site-packages/scipy/linalg/basic.pyc
>>> in inv(a, overwrite_a, check_finite)
>>>     685         inv_a, info = getri(lu, piv, lwork=lwork, overwrite_lu=1)
>>>     686     if info > 0:
>>> --> 687         raise LinAlgError("singular matrix")
>>>     688     if info < 0:
>>>     689         raise ValueError('illegal value in %d-th argument of
>>> internal '
>>>
>>> LinAlgError: singular matrix
>>> ------------------------------------------------------------
>>> ---------------
>>>
>>> Thanks in advance!
>>>
>>> Best,
>>> Emma
>>>
>>> ------
>>> Emma(Wei) Chen, Ph.D.
>>> Objects and Knowledge Laboratory
>>> New York University Abu Dhabi
>>> PO Box 129188
>>> Abu Dhabi, United Arab Emirates
>>>
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-- 
------
Emma(Wei) Chen, Ph.D.
Objects and Knowledge Laboratory
New York University Abu Dhabi
PO Box 129188
Abu Dhabi, United Arab Emirates
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