I am using generalized linear model in statsmodels to fit biological data, which follows the Gamma distribution.
My question is can we make glm to allow a pre-assigned starting coefficient?
I know the starting point is calculated by "family.starting_mu()": (y+y.mean())/2,
Could it be useful to have a small change at line 386 in generalized_linear_model.py
if starting_coefficient is Null:
mu = self.family.starting_mu(self.endog)
mu = np.dot(exog, starting_coefficient)
I saw the similar question raised before:
But I didn't get the point.
The reason for having this, is sometimes we do glm in a loop, the starting coefficient for the current iteration is taking from the previous fitting, namely res_previous.params.
This renders more power to GLM.
Closing as a duplicate of #443. We don't yet take start_params for GLM, but it should be pretty simple to fix. Feel free to submit a PR, or maybe I can have a look at it since it really shouldn't be much work.