Coding week #5

mehaksachdeva
Published: 07/04/2019

What did I do this week?

This week I tested the Binomial MGWR model using a simulated dataset with three cases - single independent variable, multiple independent variables and a global model to check with the Binomial global model. The test results compared to the existing GWR model but the bandwidths observed were not as expected for both. The scores for AIC, AICc and BIC were similar for the MGWR model as for the GWR model. After theoretical research on Binomial GAMs, I concluded that the approach was in sync with the theory and should result in comparable results. I also edited the Poisson model tests and example notebooks this week. I also performed a Monte Carlo simulation for it to test the output distribution of the parameters. The binomial model though on track, needs further exploration and resolution of a few outstanding tasks. I am maintaining the Pull request for the simulated data example for Poisson MGWR and the code base changes to accommodate these models (https://github.com/pysal/mgwr/pull/60 ).

What is coming up next?

In the coming week I will work on testing the Binomial MGWR model with simulated data and finalize the approach for the Binomial model. I will also plan for the next part of the project which is to enable predictions from the model at unsampled locations and think about how to introduce that functionality.

Did I get stuck anywhere?

No major issues were encountered in testing the Poisson and Binomial model using simulated data this week. The results were as expected and comparable to the GWR and global models. The Binomial MGWR model still needs more example cases which will be updated till next week.

Looking forward to the progress update next week!

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