A plea for less word clouds

April 25, 2013
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A plea for less word clouds

Word cloud of DOMA hearing transcriptsI must admit, there is something appealing about the word cloud - that is, until you try to understand what it actually means...Word clouds are pervasive - even in the science world. I was somewhat spurred to wri...

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A plea for less word clouds

April 25, 2013
By
A plea for less word clouds

Word cloud of DOMA hearing transcriptsI must admit, there is something appealing about the word cloud - that is, until you try to understand what it actually means...Word clouds are pervasive - even in the science world. I was somewhat spurred to write...

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interesting puzzle

April 25, 2013
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interesting puzzle

In addition to its weekly mathematics puzzles, Le Monde is now publishing a series of vulgarisation books on mathematics, under the patronage of Cédric Villani. Jean-Michel Marin brought me two from the series, one on the golden number and one on Pythagoras’ theorem. (This is actually a translation of a series published by El Pais

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BEER REVIEW: End of the World Midnight Wheat

April 24, 2013
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BEER REVIEW: End of the World Midnight Wheat

I promised in Episode 166 that I’d review this beer with a bit more detail than my usual quick spiel on the show, so allow me to present: End of the World Midnight Wheat! An ale brewed with “midnight wheat, chocolate malt, chili and spice” from Shock Top (aka Anheuser-Busch) First and foremost a special

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A brainfuck interpreter for R

April 24, 2013
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A brainfuck interpreter for R

The deadline for my book on R is fast approaching, so naturally I’m in full procrastination mode.  So much so that I’ve spent this evening creating a brainfuck interpreter for R.  brainfuck is a very simple programming language: you get an array of 30000 bytes, an index, and just 8 eight commands.  You move the

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Free e-Copy of Bayesian Computation with R (Use R)

April 24, 2013
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Free e-Copy of Bayesian Computation with R (Use R)

Amazon is currently making the first edition of Bayesian Computation with R (Use R) by Jim Albert available for free on Kindle. I own a copy of the book and there is a lot of good content and R examples on how one can do general Bayesian statistics.  The R scripts  from the book (2nd edition but

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Stamen maps with spplot

Stamen maps with spplot

Several R packages provide an interface to query map services (Google Maps, Stamen Maps or OpenStreetMap) to obtain raster images …Continuar leyendo »

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Announcing Revolution R Enterprise 6.2

April 24, 2013
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Announcing Revolution R Enterprise 6.2

We are pleased to announce that Revolution R Enterprise Release 6.2 is available to new subscribers today. This new software release from Revolution Analytics includes a number of key new features: Support for open source R 2.15.3, the latest stable release of R. Since Release 2.14.2, the R Project has added 89 new features, 11 performance enhancements and 139...

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Bank of England Fan Charts in R

April 24, 2013
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Bank of England Fan Charts in R

I managed to catch David Spiegelhalter’s Tails You Win on BBC iplayer last week. I missed it the first time round, only for my parents on my last visit home to tell me about a Statistician jumping out of a … Continue reading →

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Prefer = for assignment in R

April 23, 2013
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Prefer = for assignment in R

We share our opinion that = should be preferred to the more standard <- for assignment in R. This is from a draft of the appendix of our upcoming book. This has the risk of becoming an R version of Javascript’s semicolon controversy, but here you have it. R has five common assignment operators: “=“, Related posts:

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Disaggregating Annual Losses into Each Quarter

April 23, 2013
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Disaggregating Annual Losses into Each Quarter

In loss forecasting, it is often necessary to disaggregate annual losses into each quarter. The most simple method to convert low frequency to high frequency time series is interpolation, such as the one implemented in EXPAND procedure of SAS/ETS. In the example below, there is a series of annual loss projections from 2013 through 2016.

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Learn how to analyze data with R with Coursera’s "Data Analysis" videos

April 23, 2013
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If you didn't manage to catch Coursera's Data Analysis course, don't despair. Instructor Jeff Leek has made the course videos available on YouTube, which you can review at your leisure to learn how to plan, carry out, and communicate analyses of real data sets with R. (The course assumes you already have familiarity with R, so if you're new...

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Python Complements R’s Shortcomings

April 23, 2013
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Python Complements R’s Shortcomings

I’m a big fan of open-source software for research. For example, R-statistics, Qgis, and Grass GIS are awesome programs. R can do any statistical tests and numerical modeling you can imagine; if there’s not a built-in function you can write … Continue reading →

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Slides from my R intro seminar

April 23, 2013
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Slides from my R intro seminar

Here are my slides from a short introductory seminar on R (essentially going through part I of the R tutorial) last week. As magic lantern pictures go, they’re hideously ugly, but they were mostly there for future reference. Most of the seminar was spent showing RStudio. This Friday, we’ll practice some uses of qplot and make

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The Foundation for Open Access Statistics

April 23, 2013
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Earlier this month we blogged about Harvard Professors Gary King and Stuart Shieber providing advice to graduate students about open access, dissertations, and journal publishing. We also mentioned some of the great initiatives that facilitate open access publishing in the statistics community, like the Journal of Statistical Software (JSS), The R Journal and arxiv.org. The ...

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wapply: A faster (but less functional) ‘rollapply’ for vector setups

April 23, 2013
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wapply: A faster (but less functional) ‘rollapply’ for vector setups

For some cryptic reason I needed a function that calculates function values on sliding windows of a vector. Googling around soon brought me to ‘rollapply’, which when I tested it seems to be a very versatile function. However, I wanted to code my own version just for vector purposes in the hope that it may

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Review: Kölner R Meeting 12 April 2013

April 23, 2013
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Review: Kölner R Meeting 12 April 2013

Our 5th Cologne R user group meeting was the best attended meeting so far, with 20 members finding their way to the Institute of Sociology for two talks by Diego de Castillo on shiny and Stephan Holtmeier on cluster analysis, followed by beer and schnitzel at the Lux, a gastropub nearby.ShinyDiego gave an overview of...

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Installation of WRS package (Wilcox’ Robust Statistics)

April 22, 2013
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Some users had trouble installing the WRS package from R-Forge. Here’s a method that should work automatically and fail-safe: ?View Code RSPLUS# first: install dependent packages install.packages(c("MASS", "akima", "robustbase"))   # second: install suggested packages install.packages(c("cobs", "robust", "mgcv", "scatterplot3d", "quantreg", "rrcov", "lars", "pwr", "trimcluster", "parallel", "mc2d", "psych", "Rfit"))   # third: install WRS install.packages("WRS", repos="http://R-Forge.R-project.org",

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Scripts and Functions: Using R to Implement the Golden Section Search Method for Numerical Optimization

Scripts and Functions: Using R to Implement the Golden Section Search Method for Numerical Optimization

In an earlier post, I introduced the golden section search method – a modification of the bisection method for numerical optimization that saves computation time by using the golden ratio to set its test points.  This post contains the R function that implements this method, the R functions that contain the 3 functions that were

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The Golden Section Search Method: Modifying the Bisection Method with the Golden Ratio for Numerical Optimization

The Golden Section Search Method: Modifying the Bisection Method with the Golden Ratio for Numerical Optimization

Introduction The first algorithm that I learned for root-finding in my undergraduate numerical analysis class (MACM 316 at Simon Fraser University) was the bisection method.  It’s very intuitive and easy to implement in any programming language (I was using MATLAB at the time).  The bisection method can be easily adapted for optimizing 1-dimensional functions with

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Adding Percentiles to PDQ

April 22, 2013
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Adding Percentiles to PDQ

Pretty Damn Quick (PDQ) performs a mean value analysis of queueing network models: mean values in; mean values out. By mean, I mean statistical mean or average. Mean input values include such queueing metrics as service times and arrival rates. These could be sample means. Mean output values include such queueing metrics as waiting time and queue...

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Upcoming GDAT Class May 6-10, 2013

April 22, 2013
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Upcoming GDAT Class May 6-10, 2013

Enrollments are still open for the Level III Guerrilla Data Analysis Techniques class to be held during the week May 6—10. Early-bird discounts are still available. Enquire when you register. As usual, all classes are held at our lovely Larkspur...

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Gridding data for multi-scale macroecological analyses

April 22, 2013
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Gridding data for multi-scale macroecological analyses

These are materials for the first practical lesson of the Spatial Scale in Ecology course. All of the data and codes are available here. The class covered a 1.5h session. R code for the session is also at the end…Read more →

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Time Varying Higher Moments with the racd package.

April 22, 2013
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Time Varying Higher Moments with the racd package.

The Autoregressive Conditional Density (ACD) model of Hansen (1994) extended GARCH models to include time variation in the higher moment parameters. It was a somewhat natural extension to the premise of time variation in the conditional mean and variance, though it probably raised more questions than it, or subsequent research have been able to answer.

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Time Varying Higher Moments with the racd package.

April 22, 2013
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Time Varying Higher Moments with the racd package.

The Autoregressive Conditional Density (ACD) model of Hansen (1994) extended GARCH models to include time variation in the higher moment parameters. It was a somewhat natural extension to the premise of time variation in the conditional mean and variance, though it probably raised more questions than it, or subsequent research have been able to answer.

Read more »

Veterinary Epidemiologic Research: Count and Rate Data – Poisson & Negative Binomial Regressions

April 22, 2013
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Veterinary Epidemiologic Research: Count and Rate Data – Poisson & Negative Binomial Regressions

Still going through the book Veterinary Epidemiologic Research and today it’s chapter 18, modelling count and rate data. I’ll have a look at Poisson and negative binomial regressions in R. We use count regression when the outcome we are measuring is a count of number of times an event occurs in an individual or group

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R et Twitter

April 22, 2013
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R et Twitter

(This article was first published on Learning Data Science , and kindly contributed to R-bloggers) On va dans ce post, illustrer une utilisation simple des packages twitteR, StreamR, tm qui permettent faire du textmining. En réalité, les deux premiers permettent de récuperer les tweets et de faire des comptages simples et complexes et le dernier permet de faire du...

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2D plot with histograms for each dimension (2013 edition)

April 22, 2013
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2D plot with histograms for each dimension (2013 edition)

In 2009, I wrote about a way to show density plots along both dimensions of a plot. When I ran the code again to adapt it to a new project, it didn't work because ggplot2 has become better in the meantime. Below is the updated code. Using the gridExtra...

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Time series data in R

April 22, 2013
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Time series data in R

Handling time series data in R In this blog post I want to write some thoughts about handling time series data in R. In contrast to cross-sectional data, in time series applications each observation has an additional component besides it’s value: the point of time. This requires some additional efforts, for example: x-axis has to

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