Did you ever want to know how to unhide the toolbar in MPLD3? Here is how: http://stackoverflow.com/questions/29976625/unhide-toolbar-in-mpld3
Monthly Archives: May 2015
Unhide a toolbar in MPLD3
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Filed under dataviz, Uncategorized
Interesting read: Why Stochastic Can Be a Dirty Word
A taxonomy of these public responses is apparent: relief (as predicted by the authors of the article) that they did nothing to give themselves cancer, skepticism about the author’s motives, doubt about the accuracy of the science, a belief that the science must be wrong because cancer cannot be random, and anguish about their cancer being deprived of meaning. The last 2 responses often appear together.
http://jamanetwork.com/article.aspx?doi=10.1001/jamaoncol.2015.0786
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Filed under health communication
What was I up to two year ago?
One fun thing about keeping my lab notebook in digital form with IPython Notebooks is that I can flip through my old work so easily. Did I say fun? I meant scary, and sometimes depressing. But yes, also fun.
For example, two years ago, I was working on some projects that are still not wrapped up today, and I was doing a lot of prep for the first edition of my now re-titled “machine learning for health metricians” class.
Hey that includes the answer to [a question someone just asked on stats.stackexchange](http://stats.stackexchange.com/q/149801/18291)
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Filed under machine learning
Jupyter Notebooks in GitHub
So cool:
https://github.com/blog/1995-github-jupyter-notebooks-3
https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks
I wonder what diffs look like?
Currently, not shown: https://github.com/fonnesbeck/statistical-analysis-python-tutorial/commit/17ca0cd15c1379f9adc4561042c4a31621baeef6
Is that next GitHub? It will be huge.
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Filed under software engineering
Irreproducibile science as a communication failure
From: Abraham D. Flaxman
Sent: Thursday, May 7, 2015 4:40 PM
To: reproducible@u.washington.edu
Subject: [Reproducible] licenses and reproducibility: the scholarly communication lens
The recent discussion on reproducibility and licensing inspired me to read something historical about UW and software licensing that has been on my desk for a while. I think others on the list might find it interesting as well, so I scanned a copy for you: https://www.dropbox.com/s/79k92iwm20159of/williams_barnett_digital_ventures_2009.pdf?dl=0
I particularly like the idea that software is communication, and the university is an institute that is good at scholarly communication and at teaching. I think there is some framing here that could be valuable for reproducible research as well. Irreproducible results are, in a sense, a communication failure, and a lot of what we are talking about on this list are different ways to improve our scholarly communication.
–Abie
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Filed under science policy
Why do we call it “ridge” regression?
Asked and answered: http://stats.stackexchange.com/a/151351/18291
With a link to more detail: http://www.itl.nist.gov/div898/handbook/pri/section3/pri336.htm
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Filed under machine learning
A post on a talk on the book Epic Measures
I got my high school buddy to write a book about my boss… what could go wrong? They were at Town Hall Seattle a few weeks ago, and I think nothing did: http://townhallseattle.org/event/jeremy-smith-and-christopher-murray/
Is there a recording online somewhere?
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Filed under global health
I like the term OneHotEncoder
Dummy variable just sounds demeaning to me. http://stats.stackexchange.com/questions/149122/treating-missing-data-in-voting-pattern-analysis/149572#149572
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Filed under machine learning
How did I end up reading a 30 year old book on density estimation?
Simple, I wanted to make violin plots for efficiency scores, and they shouldn’t have any density below zero. Here is a sneaky way to sneak such a figure out of Python/Seaborn: https://github.com/mwaskom/seaborn/issues/525#issuecomment-97651992
The truncation makes them look more like gyro meat than violins.
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Filed under dataviz
By no means unhelpful
Good advice from Density Estimation for Statistics and Data Analysis by Bernard. W. Silverman:
Filed under statistics
Healthy Algorithms 