## A world without referees

In an *invited contribution* to the last ISBA Bulletin, Larry Wasserman discusses the “almost 350 years old” peer review system (paper). Have a look on it, it’s quite thought provoking!

We should think about our field like a marketplace of ideas. Everyone should be free to put their ideas out there. There is no need for referees. Good ideas will get recognized, used and cited. Bad ideas will be ignored. This process will be imperfect. But is it really better to have two or three people decide the fate of your work?

A world where you put your work on arXiv or on your web page, where you save so much time, isn’t it tempting?

## Rochebrune Workshop 2012

Hey,

Last week I attended Rochebrune workshop for the second time. The genius organizers’ idea (Liliane Bel and Eric Parent from AgroParisTech, Jean-Jacques Borreux from Liège University) is to mix *ski, stats and spirits* (mostly Genepi and Chartreuse) around a remote alpine chalet on top of Megève ski resort.

Most of the attendees are (young) Bayesians working in applied fields, ranging from biology, ecology and epidemiology, to meteorology and climatology. We had great talks about fishes, trees, birds (Joël’s busard cendré), drugs and avalanches. More methodological talks dealt with extremes, Bayesian model averaging, and simulations: variational approximations, INLA, ABC, and MCMC in general. We had a tutorial about JAGS and WinBugs/OpenBugs as well (and how to interface them with R using rjags and R2WinBUGS). I presented my work about Multidimensional covariate dependent Dirichlet processes (all presentations here).

In addition to the 10 talks per day, a sacrosanct 5-hour skiing slot was reserved in the afternoon, with lessons from crazy Mégevan instructors. They must be really good: Pierre, don’t be afraid, I jumped and felt significantly less than two years ago. Have a Chartreuse, cheers!

## Valentine Day and lonely people in France

Insee published recently a paper (in French), well in line with the Valentine Day, which characterizes people living alone or in couple by socio-professional category, along with the data.

Between 1990 and 2008 (two population surveys), the proportion of people living alone mostly increased for people under 60. After 60, 38% of women live alone, for only 17% of men, because women are married to older men, and live longer than them, in average. See that proportion by age:

Spatially, there is a kind of South/North opposition. During the working life, lonely people live in the South (left), while lonely retired people live in the North (right), with an exception for Île-de-France (Paris) with a high proportion whatever the age:

## Daily casualties in Syria

Every new day brings its statistics of new deaths in Syria… Here is an attempt to learn about the Syrian uprising by the figures. Data vary among sources: the Syrian opposition provides the number of casualties by day (here on Dropbox), updated on 8 February 2012, with a total exceeding 8 000.

We note first that the attacks accelerate, as the cumulated graph is mostly convex (click to enlarge):

Plotting the numbers by day shows the bloody situation of Fridays, a gathering day in the Muslin calendar. This point was especially true at the beginning of the uprising, but lately any other day can be equally deadly:

There are almost twice as much deaths on Fridays as any other day in average:

Here are boxplots for the logarithm of daily casualties by day of the week:

and their density estimates, first coloured by day of the week, then by Friday vs rest of the week:

Here is the code (with clumsy parts for fitting the data frames for ggplot, do not hesitate to comment on it)

library(ggplot2) input=read.csv("http://dl.dropbox.com/u/1391912/Blog%20statisfaction/data/syria.txt", sep="\t",header=TRUE,stringsAsFactors=FALSE) input$LogicalFriday=factor(input$WeekDay =="Friday",levels = c(FALSE, TRUE), labels = c("Not Friday", "Friday")) input$Date=as.Date(input$History,"%d/%m/%Y") input$WeekDays=factor(input$WeekDay, levels=unique(as.character(input$WeekDay[7:13]))) # trick to sort the legend qplot(x=Date,y=cumsum(Number), data=input, geom="line",color=I("red"),xlab="",ylab="",lwd=I(1)) qplot(x=as.factor(Date),y=Number, data=input, geom="bar",fill=LogicalFriday,xlab="",ylab="") qplot(log(Number+1), data=input, geom="density",fill=LogicalFriday,xlab="",ylab="",alpha=I(.2)) qplot(log(Number+1), data=input, geom="density",fill=WeekDay,xlab="",ylab="",alpha=I(.2)) qplot(WeekDays,log(Number+1),data=input,geom="boxplot",xlab="",ylab="",colour=WeekDays)

## Coming R meetings in Paris

If you live in Paris and are interested in R, there will be two meetings for you this week.

First a Semin-R session, organized at the Muséum National d’Histoire Naturelle on Tuesday 7 Feb (too bad, the Museum is closed on Tuesdays). Presentations will be about colors, phylogenies and maps, while I will speak about (my beloved) RStudio. The slides of previous sessions can be found here (most of them are in French).

The following day, 8 Feb, a group of R users from INSEE will have its first meeting (13-14h, INSEE, room R12), about SQLite data in R, maps, and in R.

I guess anyone can join!

UPDATE: Here is a colorful map to access INSEE . Come with an ID, and say you are visiting the meeting organizer Matthieu Cornec. Room R12 is on the ground floor (left).

## Psycho dice and Monte Carlo

Following Pierre’s post on *psycho dice*, I want here to see by which average margin repeated plays might be called *influenced by mind will*. The rules are the following (exerpt from the novel *Midnight in the Garden of Good and Evil*, by John Berendt):

You take four dice and call out four numbers between one and six–for example, a four, a three, and two sixes. Then you throw the dice, and if any of your numbers come up, you leave those dice standing on the board. You continue to roll the remaining dice until all the dice are sitting on the board, showing your set of numbers. You’re eliminated if you roll three times in succession without getting any of the numbers you need. The object is to get all four numbers in the fewest rolls.

Simplify the game by forgetting the elimination step. Suppose first one plays with an even dice of *1/p* faces. The probability of it to show the right face is *p* (for somebody with no *psy* power). Denote *X* the time to first success with one dice, which follows, by independence, a geometric distribution Geom(*p*) (with the starting-to-1 convention). *X* has the following probability mass and cumulative distribution functions, with *q=1-p*:

Now denote *Y* the time to success in the game with *n* dice. This simultaneous case is the same as playing *n* times independently with 1 dice, and then taking *Y* as the sample maximum of the different times to success. So *Y*‘s cdf is

Its pmf can be obtained either exactly by difference, or up to a normalizing constant *C* by differentiation:

As it is not too far from the Geom(*p*) pmf, one can use the latter as the proposal in a Monte Carlo estimate. If ‘s are *N* independent Geom(*p*) variables, then

and

The following R lines produce the estimates and .

Created by Pretty R at inside-R.org

Now it is possible to use a test (from classical test theory) to estimate the average margin with which repeated games should deviate in order to detect statistical evidence of *psy* power. We are interested in testing against , for repeated plays.

If the game is played *k* times, then one rejects if the sampled mean is less than , where is the 95% standard normal quantile. To indicate the presence of a *psy* power, someone playing times should perform in 2 rolls less than the predicted value (in 1 roll less if playing times). I can’t wait, I’m going to grab a dice!

## Create maps with maptools R package

Baptiste Coulmont explains on his blog how to use the R package maptools. It is based on shapefile files, for example the ones offered by the French geography agency IGN (at départements and communes level). Some additional material like roads and railways are provided by the OpenStreetMap project, here. For the above map, you need to dowload and dezip the files departements.shp.zip and ile-de-france.shp.zip. The red dots correspond to points of interest longitude / latitude, here churches stored in a vector *eglises* (use e.g. this to geolocalise places of interest). Then run this code from Baptiste’s tutorial

library(maptools) france<-readShapeSpatial("departements.shp", proj4string=CRS("+proj=longlat")) routesidf<-readShapeLines( "ile-de-france.shp/roads.shp", proj4string=CRS("+proj=longlat") ) trainsidf<-readShapeLines( "ile-de-france.shp/railways.shp", proj4string=CRS("+proj=longlat") ) plot(france,xlim=c(2.2,2.4),ylim=c(48.75,48.95),lwd=2) plot(routesidf[routesidf$type=="secondary",],add=TRUE,lwd=2,col="lightgray") plot(routesidf[routesidf$type=="primary",],add=TRUE,lwd=2,col="lightgray") plot(trainsidf[trainsidf$type=="rail",],add=TRUE,lwd=1,col="burlywood3") points(eglises$lon,eglises$lat,pch=20,col="red")

## Google Fusion Tables

A quick post about another Google service that I discovered recently called Fusion Tables. There you can store, share and visualize data up to 250 MB, of course in the cloud. With Google Docs, Google Trends and Google Public Data Explore, it is another example of Google’s efforts to gain ground in data management. Has anyone tried it out?

## France open data at data.gouv.fr

Today is launched the (*beta* version of the) brand new French website for open data, at data.gouv.fr (do not misunderstand the url, it is in French!). On prime minister’s initiative, it collects data from various ressources, among which the institute for statistics INSEE, most of the ministries (Finance, Culture, etc), several big companies (like the state-owned railway company SNCF) for an *open, transparent and collaborative model of governance*. Datasets are available in CSV or Excel spreadsheet, under open licence for most of them, and should be updated frequently (monthly).

Some of the datasets you can find: list of 3000+ train stations with geolocalisation (later with traffic?); geolocalisation of road accidents; the comprehensive list of books in *Bibliothèque Nationale de France* (which scans and stores each and every book published in France); the number of students in Latin and ancient Greek classes; France national budget, etc… among 350 000 others.

I have added the link in our datasets page.

## Power-laws: choose your x and y variables carefully

*This is a follow-up of the post Power of running world records*

As suggested by Andrew, plotting running world records could benefit from a change of variables. More exactly the use of different variables sheds light on a [now] well-known [to me] sports result provided in a 2000 Nature paper by Sandra Savaglio and Vincenzo Carbone (thanks Ken): the dependence between time and distance in log-log scale is not linear on the whole range of races, but *piecewise linear*. There is one break-point around time 2’33’’ (or equivalently distance around 1100 m). As mentioned in the article, this threshold corresponds to a physiological critical change in the athlete’s energy expenditure: in short races (less than 1000 m) the effort follows an anaerobic metabolism, whereas it switches to aerobic metabolism for middle and long distances (or longer…). Interestingly, the energy is more efficiently consumed in the second regime than in the first: the decay in speed slows down for endurance races.

The reason of this graphical/visual difference is simple. Denote distance, time and speed by D, T and S. I have plotted the log T~ log D relation, which gave with . When using the speed S as one of the variables, the relations are and with and to the first order because is close to 1. With Nature paper findings (with the opposite sign convention), the two s are (anaerobic) and (aerobic), ie and . My improper is indeed in between. The slope ratio is much larger (larger than 2) on a plot involving the speed, clearly showing the two regimes, than on my original plot (a few 10%), which is the reason why it appear almost linear (although afterthought, and with good goggles, two lines might have been detected).

Below is the S ~ log D relation (click to enlarge) on which it appears clearly that 100 m and 100 km races are two outliers. It takes time to erase the loss of time due to the start of the race (100 m and 200 m are run at the same speed…), whereas the 100 km suffers from a lack of interest among athletes.Achim Zeileis also provides an extended world records table and R code in his comment.

As an aside, Andrew and Cosma Shalizi also comment and resolve an ambiguity of mine: one usually speaks about power-laws without much precision of context, but there are mainly two separate sets of power-law models. Either power-law regressions, where you plot y~x for two different variables (this is the case here); or power-law distributions, ie the probability distribution of a single variable x is , or extensions of that (with lots of natural examples, ranging from the size of cities to the number of deaths in attacks in wars).

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