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Kenneth Feinberg explains his approach to deciding how to distribute funds for victims of tragedies such as the Boston Marathon bombings.

(This article was first published on factbased, and kindly contributed to R-bloggers) Last weekend I submitted an update of my R package datamart to CRAN. It has been more than a half year since the last update, however there are only minor advances. The package is still in its early stages, and very experimental.One new feature is the function uconv. Think iconv, but instead of converting character vectors between different encodings, this function converts numerical vectors between different units of measurements. Now if you want to know how many centimeters one horse length is, you can write in R:> #install.packages("datamart") > library(datamart) > uconv(1, "horse length", "cm") and you will get the answer 240. I had the idea for this function when I had to convert between various energy units, including natural units of energy fuels like cubic metres of natural gas. The uconv function supports this, using common constants for the conversion.> uconv(1, "Mtoe", "PJ") [1] 41.88 > uconv(1, "m³ NG", "kWh") [1] 10.55556 These conversions may be ambigious. For instance, the last one combines a volume and an energy dimension. An optional parameter allows the specification of the context, or unitset:> uconv(1, "Mtoe", "PJ", uset="Energy") The currently available unit sets and units therein can be inspected with> uconvlist() The first argument can be a numerical vector:> set.seed(13) > uconv(37+2*rnorm(5), "°C", "°F", uset="Temperature") [1] 100.59558 97.59102 104.99059 99.27435 102.71309 To leave a comment for the author, please follow the link and comment on his blog: factbased. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

“Indoctrinating children in proper environmental thought was a hallmark of the green movement.” M. Crichton, micro, p. ix I believe I read most of Michael Crichton‘s novels and this posthumous version (completed by Richard Preston) is not very different in its style and pattern from the previous ones. micro delivers an efficient fast-paced techno-thriller that filled most of one afternoon when convalescing at home.  In that respect, it fills its intended role. I however feel this is one of the weakest novels in that the technological and scientific background is very poor. (The best Crichton’s novels are in my opinion The Andromeda Strain and Airframe. One of the last novels, State of Fear, carries a very anti-environmentalist and climatoskeptic  message similar to the above quote.) “Perhaps the most important lesson to be learned by direct experience is that the natural world (…) represents a complex system and therefore we cannot understand it and we cannot predict its behavior. “ M. Crichton, micro, p. x Indeed, the plot of micro is based on the assumption that there exists a technology that can miniaturise living and non-living objects to 1/100th of their original size without any short-term impact. I remember watching as a child Fantastic Voyage, where a miniaturised submarine goes inside a blood vessel to remove a tumor, and I sat in front of a neighbour’s TV, mesmerised by the idea more than by the (weak) plot. This was in the laste 60′s. I also remember a sci’fi’ book I read when a pre-teen, with a great cover, called The Forgotten Planet: nothing truly memorable, apart from the cover, but hey this was a 1954 book. Now, micro does not use a deeper theory to justify this miniaturisation and the remainder of the plot is just as weak: I cannot imagine  1/100th humans surviving more than a few minutes in a rain forest environment! The place is crawling with insects, all way faster and far more deadly than tiny humans with a pocket knife, but the heroes conveniently meet only one dangerous insect at a time, loosing only at most one member of the group each time (sorry for the spoiler!). (In fact, the earlier Prey was much better at involving nanotechnologies. ) The grad students are very charicaturesque as well, providing biological infodump at times when they should be frozen solid with fright. Provided they had not been eaten already. The final resolution of the thriller is just… grotesque! So wait until you are sick or recovering from being sick before embarking upon this micro and no so fantastic trip! Filed under: Books, Kids Tagged: book review, Michael Crichton, micro, science fiction, techno-thriller

(This article was first published on The stupidest thing... » R, and kindly contributed to R-bloggers) I was playing Chutes & Ladders with my four-year-old daughter yesterday, and I thought, “How long is this going to take?” I saw an interesting mathematical analysis of the game a few years ago, but it seems to be offline, though you can read it via the wayback machine. But that didn’t answer my specific question, namely, “How long is this going to take?” So I wrote a bit of R code to simulate the game. Here’s the distribution of the number of spins to complete the game, by number of players: With two players, the average number of spins is 52, with a 90th percentile of 88. If you add a third player, the average increases to 65, and the 90th percentile increases to 103. You’re playing fewer rounds, but each round is three times as long. If you add a fourth player, the average is 76 and the 90th percentile is 117. So, in trying to minimize the agony, it seems best to not encourage my eight-year-old son to join us in the game. If he plays with us, there’s a 63% chance that it will take longer. And that’s particularly true because then the chance of my daughter winning drops from about 1/2 to about 1/3. That raises another question: if I let her go first, what advantage does that give her? Not much. The chance that the person who goes first winning is 50.9%, 34.4%, and 25.9%, respectively, when there are 2, 3, and 4 players. So not a noticeable amount. Thus I cheat (on her behalf). Really, thought, I’m cheating in order to shorten the game as much as to ensure that she wins. Note: There’s a close connection between this problem and my work on the multiple-strain recombinant inbred lines. (See this and that.) I’m tempted to play around with it some more. To leave a comment for the author, please follow the link and comment on his blog: The stupidest thing... » R. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

Ahh, arguing on the Internet. Whether it's about politics, religion, culture or science, if you're anything like me you've had 1000 online arguments and changed the mind of exactly nobody. Still, it's fun to exercise those debating muscles, and now with the handy website thou shalt not commit logical fallacies you have the perfect riposte to any invalid, inappropriate or just plain wrong argument (click for the full version): Someone impugning your character instead of responding to your well-reasoned point? Send 'em over to Ad-Hominem land. Asking you to prove a negative, that they introduced? That's not where the burden of proof lies, buddy. You might say that no true debater would use a tool like this, but I say that's just moving the goalposts. Each of the fallacies comes with a pithy example. (Begging the Question: "The word of Zorbo the Great is flawless and perfect. We know this because it says so in The Great and Infallible Book of Zorbo's Best and Most Truest Things that are Definitely True and Should Not Ever Be Questioned."). You can find all of the fallacies and examples by downloading the poster, which comes under a sharing-friendly Creative Commons license. You can also explore each of the fallacies at yourlogicalfallacyis.com. Have a great weekend!

(This article was first published on Data and Analysis with R, at Work, and kindly contributed to R-bloggers) In my last post I said that I would try to investigate the question of who actually does want a casino, and whether place of residence is a factor in where they want the casino to be built.  So, here goes something: The first line of attack in this blog post is to distinguish between people based on their responses to the third question on the survey, the one asking people to rate the importance of a long list of issues.  When I looked at this list originally, I knew that I would want to reduce the dimensionality using PCA. library(psych) issues.pca = principal(casino[,8:23], 3, rotate="varimax",scores=TRUE) The PCA resulted in the 3 components listed in the table below.  The first component had variables loading on to it that seemed to relate to the casino being a big attraction with lots of features, so I named it “Go big or Go Home”.  On the second component there seemed to be variables loading on to it that related to technical details, while the third component seemed to have variables loading on to it that dealt with social or environmental issues. Go Big or Go Home Concerned with Technical Details Concerned with Social/Environmental Issues or not Issue/Concern Q3_A 0.181 0.751 Design of the facility Q3_B 0.366 0.738 Employment Opportunities Q3_C 0.44 0.659 Entertainment and cultural activities Q3_D 0.695 0.361 Expanded convention facilities Q3_E 0.701 0.346 Integration with surrounding areas Q3_F 0.808 0.266 New hotel accommodations Q3_G -0.117 0.885 Problem gambling & health concerns Q3_H 0.904 Public safety and social concerns Q3_I 0.254 0.716 Public space Q3_J 0.864 0.218 Restaurants Q3_K 0.877 0.157 Retail Q3_L 0.423 0.676 -0.1 Revenue for the city Q3_M 0.218 0.703 0.227 Support for local businesses Q3_N 0.647 0.487 -0.221 Tourist attraction Q3_O 0.118 0.731 Traffic concerns Q3_P 0.497 0.536 0.124 Training and career development Once I was satisfied that I had a decent understanding of what the PCA was telling me, I loaded the component scores into the original dataframe. casino[,110:112] = issues.pca$scores names(casino)[110:112] = c("GoBigorGoHome","TechnicalDetails","Soc.Env.Issues") In order to investigate the question of who wants a casino and where, I decided to use question 6 as a dependent variable (the one asking where they would want it built, if one were to be built) and the PCA components as independent variables.  This is a good question to use, because the answer options, if you remember, are “Toronto”, “Adjacent Municipality” and “Neither”.  My approach was to model each response individually using logistic regression. casino$Q6[casino$Q6 == ""] = NA casino$Q6 = factor(casino$Q6, levels=c("Adjacent Municipality","City of Toronto","Neither")) adj.mun = glm(casino$Q6 == "Adjacent Municipality" ~ GoBigorGoHome + TechnicalDetails + Soc.Env.Issues, data=casino, family=binomial(logit)) toronto = glm(casino$Q6 == "City of Toronto" ~ GoBigorGoHome + TechnicalDetails + Soc.Env.Issues, data=casino, family=binomial(logit)) neither = glm(casino$Q6 == "Neither" ~ GoBigorGoHome + TechnicalDetails + Soc.Env.Issues, data=casino, family=binomial(logit)) Following are the summaries of each GLM: Toronto: Adjacent municipality: Neither location: And here is a quick summary of the above GLM information: Judging from these results, it looks like those who want a casino in Toronto don’t focus on the big social/environmental issues surrounding the casino, but do focus on the flashy and non-flashy details and benefits alike.  Those who want a casino outside of Toronto do care about the social/environmental issues, don’t care as much about the flashy details, but do have a focus on some of the non-flashy details.  Finally, those not wanting a casino in either location care about the social/envir

The R core group has quickly followed up with a patch to R version 3. Announced yesterday, R 3.0.1 (code name: "Good Sport") improves serialization performance with big objects, improves reliability for parallel programming and fixes a few minor bugs. (You can find the complete list of changes in the NEWS file.) The source distribution and Windows and Linux binaries are available for download now from your local mirror, and the Mac binary will follow soon. The R core group continues to develop R version 3, and more patches can be expected over the coming year. Revolution R Enterprise will update to inclde R version 3 in late 2013. R-announce: R 3.0.1 is released

(This article was first published on Revolutions, and kindly contributed to R-bloggers) The R core group has quickly followed up with a patch to R version 3. Announced yesterday, R 3.0.1 (code name: "Good Sport") improves serialization performance with big objects, improves reliability for parallel programming and fixes a few minor bugs. (You can find the complete list of changes in the NEWS file.) The source distribution and Windows and Linux binaries are available for download now from your local mirror, and the Mac binary will follow soon. The R core group continues to develop R version 3, and more patches can be expected over the coming year. Revolution R Enterprise will update to inclde R version 3 in late 2013. R-announce: R 3.0.1 is released To leave a comment for the author, please follow the link and comment on his blog: Revolutions. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

The most recent edition of the Revolution Newsletter is out. The news section is below, and you can read the full May edition (with highlights from this blog and community events) online. You can subscribe to the Revolution Newsletter to get it monthly via email. Gaming Analytics FTW! Join us on 13Jun13 at 10:00 AM PDT for our webinar with Bill Grasso from Osolog, LLC. He’ll be discussing how gaming companies are using predictive analytics to gain insight into how players behave and to apply that knowledge to reduce churn and increase monetization opportunities. If you are a digital media professional or investor, this is the webinar for you. Register today and learn how to boost your gaming revenue. New R Training Courses. The Revolution Analytics training department has just launched new R training courses, including Data Visualization in R, Marketing Analytics with R and Using R with Hadoop. Check out the R Course Catalog for the complete list. Applications of R in Finance at R/Finance 2013. Revolution Analytics is a proud sponsor of the premier gathering of quantitative analysts, portfolio managers, risk analysts and researchers using R for finance. Join us at the R/Finance 2013 conference, which will take place May 17-18 in Chicago.  Revolution Analytics in the News: Read how American Century Investments uses graph analysis techniques to map connections among companies to improve financial results in this InformationWeek cover story, and find out why Revolution Analytics will ride the 2nd wave of predictive analytics adoption into the SMB market at CRN. What’s new in Revolution R Enterprise 6.2? Check out the new features in our webinar replay. Revolution Analytics: Newsletter Archive