Shoe Consumption in the U.S. – GGPlot2 #1

October 26, 2011
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Shoe Consumption in the U.S. – GGPlot2 #1

  This is the first in a series of blog posts in which I use the R package GGPlot2 to examine real world data. In this post, I construct a line graph of U.S. shoe consumption from 1995 to 2007. A recent survey conducted by Shop Smart magazine found that the average woman in the

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Netflix Post-mortem – How to detect Bubbles

October 26, 2011
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Netflix Post-mortem – How to detect Bubbles

Bubbles. I’m no expert in behavioral economics, but bubbles seem to be well understood (after they occur) although they seem hard to detect (at least in the eyes of outsiders and late bubble participants). This post won’t tell you how to avoid bubbles, but might give you some insight. I came across Minsky’s explanation of

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Mixed-Effects Models in R with Quantum Forest

October 26, 2011
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For anyone who wants to estimate linear or nonlinear mixed-effects models (aka random-effects models, hierarchical models or multilevel models) using the R language, the Quantum Forest blog has several recent posts that will be of interest. Written by Luis Apiolaza from the School of Forestry at the University of Canterbury in New Zealand, the blog includes a number of...

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What do your rules look like? editrules 1.8-x answers with the help of igraph

October 26, 2011
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What do your rules look like? editrules 1.8-x answers with the help of igraph

We (Edwin de Jonge and me) have recently updated our editrules package. The most important new features include (beta) support for categorical data. However, in this post I'm going to show some visualizations we included, made possible by Gabor Csardi's … Continue reading →

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How to display R code on a web page

October 26, 2011
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Starting to write a blog I need a way how to publish my R codes. One possibility would be to just add some formatting with Pretty R. Nice, but I miss a repository with all codes ever submitted and possibility to make corrections.The final solution was ...

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Covariance structures

October 26, 2011
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Covariance structures

In most mixed linear model packages (e.g. asreml, lme4, nlme, etc) one needs to specify only the model equation (the bit that looks like y ~ factors...) when fitting simple models. We explicitly say nothing about the covariances that complete … Continue reading →

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Two-sex demographic models in R

October 26, 2011
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Two-sex demographic models in R

Tom Miller (a prof here at Rice) and Brian Inouye have a paper out in Ecology (paper, appendices) that confronts two-sex models of dispersal with empirical data.They conducted the first confrontation of two-sex demographic models with empirical data on...

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Controlling multiple risk measures during construction of efficient frontier

October 26, 2011
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Controlling multiple risk measures during construction of efficient frontier

In the last few posts I introduced Maximum Loss, Mean-Absolute Deviation, and Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) risk measures. These risk measures can be formulated as linear constraints and thus can be combined with each other to control multiple risk measures during construction of efficient frontier. Let’s examine efficient frontiers computed

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PAWL package on CRAN

October 26, 2011
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PAWL package on CRAN

The PAWL package (which I talked about there, and which implements the parallel adaptive Wang-Landau algorithm and adaptive Metropolis-Hastings for comparison) is now on CRAN! http://cran.r-project.org/web/packages/PAWL/index.html which means that within R you can easily install it by typing install.packages("PAWL") Isn’t that amazing? It’s just amazing. Kudos to the CRAN team for their quickness and their

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New features in R-bloggers.com

October 26, 2011
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New features in R-bloggers.com

Hello dear R community, In the past few months I have rolled out a bunch of new features to R-bloggers, and I wanted to raise awareness to them.  Please consider giving some of these a try and leave me any feedback that you have (by leaving a comment on this post): Comments – it is now possible to leave comments in...

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Batch Processing vs. Interactive Sessions

October 26, 2011
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Batch Processing vs. Interactive Sessions

We introduced batch processing 3 weeks ago. Many people asked about differences and benefits of batch processing or interactive sessions. Lets start with the definitions: Batch Processing / Batch Jobs: Batch processing is the execution of a series of programs or only one task on a computer environment without manual intervention. All data and commands

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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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Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) risk measures

October 25, 2011
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Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) risk measures

In the Maximum Loss and Mean-Absolute Deviation risk measures post I started the discussion about alternative risk measures we can use to construct efficient frontier. Another alternative risk measures I want to discuss are Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR). I will use methods presented in Comparative Analysis of Linear Portfolio Rebalancing

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Email Netiquette

October 25, 2011
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Email Netiquette

A short piece of web-scrapping I sent as a reminder to my colleague. If you run it the result should be something like... Datatata!

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Sabermetrics Meets R Meetup

October 25, 2011
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I just ran across this post at Big Computing. On November 14th, there will be an R User meet-up in Washington, DC (Tyson's Corner) led by Mike Driscoll about using R for sabermetric analysis (linked here). I will actually be home in Maryland for a co...

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Pair trading strategy : how to use "PairTrading" package

October 25, 2011
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Pair trading strategy : how to use "PairTrading" package

Mr.Ishikawa(my old friend) and I developed "PairTrading" package, and uploaded it on CRAN.This article shows you how you can use it.The pair trading is a market neutral trading strategy and gives traders a chance to profit regardless of market conditions. The idea of this strategy is quite simple. 1 : Select two stocks(or any assets) moving similarly 2 : Short...

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Approximate Bayesian computational methods on-line

October 25, 2011
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Approximate Bayesian computational methods on-line

Fig. 4 – Boxplots of the evolution of ABC approximations to the Bayes factor. The representation is made in terms of frequencies of visits to models MA(1) and MA(2) during an ABC simulation when ε corresponds to the 10,1,.1,.01% quantiles on the simulated autocovariance distances. The data is a time

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Machine Learning Ex 5.1 – Regularized Linear Regression

October 25, 2011
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Machine Learning Ex 5.1 – Regularized Linear Regression

The first part of the Exercise 5.1 requires to implement a regularized version of linear regression. Adding regularization parameter can prevent the problem of over-fitting when fitting a high-order polynomial. Read More: 194 Words Totally

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Vanilla C code for the Stochastic Simulation Algorithm

October 24, 2011
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Vanilla C code for the Stochastic Simulation Algorithm

The Gillespie stochastic simulation algorithm (SSA) is the gold standard for simulating state-based stochastic models. If you are a R buff, a SSA novice and want to get quickly up and running stochastic models (in particular ecological models) that are not … Continue reading →

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Simple Heatmap in R with Formula One Dataset

October 24, 2011
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Simple Heatmap in R with Formula One Dataset

Now, that the 2011 F1 season is over I decided to quickly scrub the Formula 1 data of the F1.com website, such as the list of drivers, ordered by the approximate amount of salary driver is getting (top list driver is making the most, approx. 30MM) and position at the end of each race. There

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