Monthly Archives: October 2011

Machine Learning Ex 5.2 – Regularized Logistic Regression

October 25, 2011
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Machine Learning Ex 5.2 – Regularized Logistic Regression

Now we move on to the second part of the Exercise 5.2, which requires to implement regularized logistic regression using Newton's Method. Plot the data:

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treebase package on cran

October 25, 2011
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treebase package on cran

My treebase package is now up on the CRAN repository. (Source code is up, the binaries should appear soon). Here’s a few introductory examples to illustrate some of the functionality of the package. Thanks in part to new data deposition requirements at journals such as Evolution, Am Nat, and Sys Bio, and data management plan

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The Psychology of Music and the ‘tuneR’ Package

October 25, 2011
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Introduction This semester I’m TA’ing a course on the Psychology of Music taught by Phil Johnson-Laird. It’s been a great course to teach because (i) so much of the material is new to me and (ii) because the study of the psychology of music brings together so many of the intellectual tools I enjoy, including

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"Anyone planning to work with Big Data ought to learn Hadoop and R"

October 25, 2011
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Dan Woods at Forbes interviewed LinkedIn's Daniel Tunkelang about the rise of data science and on building data science teams. When asked how students today should prepare themselves to be data scientists, Tunkelang gives some good advice: When we built the data science team at LinkedIn a few years ago, we looked for raw talent, assuming that smart people...

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Catching up faster by switching sooner

October 25, 2011
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Catching up faster by switching sooner

Here is our discussion (with Nicolas Chopin) of the Read Paper of last Wednesday by T. van Erven, P. Grünwald and S. de Rooij (Centrum voor Wiskunde en Informatica, Amsterdam), entitled Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the Akaike information criterion–Bayesian information criterion dilemma. It

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Mapping Hotspots with R: The GAM

October 25, 2011
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Mapping Hotspots with R: The GAM

I've been getting a lot of questions about the method used to map the hotspots in the seasonal drunk-driving risk maps.  It uses the GAM (Geographical Analysis Machine), a way of detecting spatial clusters from two data inputs: the data of interes...

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Installing the RMySQL package on Windows 7

October 25, 2011
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So you want to get statistical? Nowadays one of the ways to go is to use R, mostly in combination with ggplot2 for generating the plots. These plots and graphs however need some data, for that we use data sources. There are a lot of data sources availa...

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Example 9.11: Employment plot

October 25, 2011
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Example 9.11: Employment plot

A facebook friend posted the picture reproduced above-- it makes the case that President Obama has been a successful creator of jobs, and also paints GW Bush as a president who lost jobs. Another friend pointed out that to be fair, all of Bush's presi...

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Consecutive number and lottery

October 25, 2011
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Consecutive number and lottery

Recently, I have been reading odd things about strategies to win at the lottery. E.g. or I wrote something a long time ago, but maybe it would be better to write another post. First, it is easy to get data on the French lotteries, including dra...

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Longitudinal analysis: autocorrelation makes a difference

October 25, 2011
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Longitudinal analysis: autocorrelation makes a difference

Back to posting after a long weekend and more than enough rugby coverage to last a few years. Anyway, back to linear models, where we usually assume normality, independence and homogeneous variances. In most statistics courses we live in a … Continue reading →

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