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In a logistic regression the significance (p) of each covariate can be calculated.

For the case of a geographically weighted logistic regression (GWLR), I currently am working on a model for event prediction. This means I do not have one global model, but a different model (i.e. different Betas) at each geographical location.

In order to choose what covariates are included in the models I need to be able to assess each covariate for its contribution to the models. Doing so by making use of a p-value seems like the obvious choice.

I am able to calculate the p-values for each covariate for each individual model. Interapolating this to a raster can result in some locations having a high p-value (covariate is not good at explaining here) and low p-value (covariate is good in explaining here). But this only marginally helps me in deciding what covariates to include in the models. For the sake of simplicity I want all the models to include the same covariates.

How can a global p-value (or some other statistic) be calculated per covariate to determine whether a covariate should, or should not, be included in the models?

Since local P-values are calculated with the covariance matrix, and the covariance matrix is based on the logit of the model (which is geographically weighted), calculation of a global p-value (if at all possible) should include a geographic weighting as well.

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    Parameter space should not change given a local fit. The parameters are fixed for the model and not locally variable. Unfortunately, with evaluation of significance and fit, you are hinting at one of the many issues with GWR. – Jeffrey Evans May 31 '17 at 13:35
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As commented by @JeffreyEvans:

Parameter space should not change given a local fit. The parameters are fixed for the model and not locally variable. Unfortunately, with evaluation of significance and fit, you are hinting at one of the many issues with GWR.

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