Some notes on them here: http://nbviewer.ipython.org/gist/aflaxman/c93489dd19cee2eabf00

# Monthly Archives: December 2014

## Interactive Horizontal Bar Charts

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Filed under dataviz, software engineering

## The challenges of counting gun deaths

This interesting article crossed my desk recently, A Year of Gun Deaths: What Slate learned from trying, and failing, to record every death by gun in America since Newtown. It is a long piece that touches on many of the things that make population health metrics hard.

It also drew my attention to an IOM report on Priorities for Research to Reduce the Threat of Firearm-Related Violence. Gun deaths are a public health problem.

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Filed under global health

## PyMC3 with PyMC2

Did you know I have a fork of PyMC3 that you can run at the same time as PyMC2? I don’t keep it up to date, but people seem to want it every once in a while. Maybe this will help someone find it: https://github.com/aflaxman/pymc

import pymc as pm2 import pymc3 as pm3

Good for head-to-head comparisons…

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Filed under software engineering

## Old Maze Making Code

Playing around with that GIS optimization stuff was a chance for me to revisit some maze-making code I wrote a few years ago: https://healthyalgorithms.com/2011/01/07/piggies-and-mazes/ I wonder what age kids it will be good for.

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Filed under combinatorial optimization

## Combinatorial Optimization for GIS

A real, applied problem in spatial epidemiology crossed my desk last week, and it turns out that it is a super-fun combinatorial optimization challenge, too.

Details here: http://gis.stackexchange.com/questions/126280/group-polygon-features-to-match-a-set-of-specifications

I don’t have time to play around with it a lot now, but I did try a little stochastic search, which makes me think that this will not be trivial to solve:

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Filed under TCS

## Bayesian Correlation in PyMC

Here is a StackOverflow question with a nice figure:

Is there a nice, simple reference for just what exactly these graphical model figures mean? I want more of them.

Filed under statistics

## Statistics in Python: Calculating R^2

I wanted to include some old-fashioned statistics in a paper recently, and did some websearching on how to calculate R^2 in Python. It’s all very touchy, it seems. Here’s what I found:

http://stats.stackexchange.com/questions/36064/calculating-r-squared-coefficient-of-determination-with-centered-vs-un-center

http://stackoverflow.com/questions/893657/how-do-i-calculate-r-squared-using-python-and-numpy

http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.linregress.html

http://forums.udacity.com/questions/100154896/why-is-r-squared-from-formula-different-than-scipy-functions-one

I eventually went with this:

%load_ext rmagic x = np.array(1/df.J) y = np.array(df.conc_rand) %Rpush x y %R print(summary(lm(y ~ x + 0)))

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Filed under statistics