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@Ziqi-Li Ziqi-Li commented Jun 21, 2018

Adding summary output for GWR and MGWR. Related to #17
Here is the notebook showing the outputs.

@Ziqi-Li Ziqi-Li mentioned this pull request Jun 21, 2018
@TaylorOshan
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Done in spglm/#7, should allow aicc to be calculated for GLMResults object.

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Ziqi-Li commented Jun 22, 2018

Thanks, just pushed a new commit reflecting that.

@TaylorOshan
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Looks good. A few potential tweaks:

  1. Will the method work for Poisson/Binomial? They have slightly different model fit diagnostics
  2. For MGWR, Should we add the corrected alpha value and corrected critical t-val for each variable next to ENP_J? This could be good to report.

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Ziqi-Li commented Jun 25, 2018

For 1, yes, it works for Poisson/Binomial, see here at the bottom, I attached a summary output for the tokyo data.
For 2. Added the 95% covariate-specific t and alpha for mgwr in the new commit, also see this in the mgwr output.

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Ziqi-Li commented Jun 25, 2018

Also, here is the output from gwr4 for the tokyo one.

@TaylorOshan TaylorOshan merged commit 72b37ca into pysal:master Jul 3, 2018
@Ziqi-Li Ziqi-Li deleted the summary branch July 3, 2018 16:42
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