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if start_params = np.array([5]) one element, 1-dim array, then the squeeze removes the 1-dim which then raises exception in shape check
statsmodels_gh\statsmodels\statsmodels\robust\robust_linear_model.py in fit(self, maxiter, tol, scale_est, init, cov, update_scale, conv, start_params, start_scale)
264 else:
265 start_params = np.asarray(start_params, dtype=np.double).squeeze()
--> 266 if (start_params.shape[0] != self.exog.shape[1] or
267 start_params.ndim != 1):
268 raise ValueError('start_params must by a 1-d array with {} '
IndexError: tuple index out of range
I ran into this for the constant only regression for resistant estimators in #9227
I don't know why there is the squeeze.
If we want or need to keep the squeeze, then a fix is to add start_params = np.atleast_1d(start_params),
I will fix it this way in my PR, where I need it now
The text was updated successfully, but these errors were encountered:
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Apr 26, 2024
if
start_params = np.array([5])
one element, 1-dim array, then the squeeze removes the 1-dim which then raises exception in shape checkI ran into this for the constant only regression for resistant estimators in #9227
I don't know why there is the squeeze.
If we want or need to keep the squeeze, then a fix is to add
start_params = np.atleast_1d(start_params),
I will fix it this way in my PR, where I need it now
The text was updated successfully, but these errors were encountered: