Usually, to illustrate the difference between S3 and S4 classes in R, I mention glm (from base) and vglm (from VGAM) that provide similar outputs, but one is based on S3 codes, while the second one is based on S4 codes. Another way to illustrate is to manipulate distributions. Consider ...

There are important discussions nowadays about data modeling, to choose between the “two cultures” (as mentioned in Breiman (2001)), i.e. either econometrics models or machine/statistical learning models. We did discuss this issue recently in Econométrie et Machine Learning (so far only in French) with Emmanuel Flachaire and Antoine ...

To explain the “optimal transport” problem, we usually start with Gaspard Monge’s “Mémoire sur la théorie des déblais et des remblais“, where the the problem of transporting a given distribution of matter (a pile of sand for instance) into another (an excavation for instance). This problem ...

This morning, I discovered an interesting statistic, America Has Been At War 93% of the Time – 222 Out of 239 Years – Since 1776,  i.e. the U.S. has only been at peace for less than 20 years total since its birth. I wanted to check, get a better understanding and look at other countries ...

For the (Advanced) R Crash Course of the Data Science for Actuaries program, we will use the Ubuntu virtual machine. There might be some issues when installing some packages… One trick can be to open a terminal and then to use the sudo command, to install some packages, (after entering ...

In two weeks, the third year of the Data Science for Actuaries program will start. I will be there for the introduction to R. The slides are available online (created with slidify) A markdown is also available. I have to thank Ewen for his help on slidify (especially for the ...

Following my previous post, I wanted to spend more time, on the time series with “global weather-related disaster losses as a proportion of global GDP” over the time period 1990-2016 that Roger Pilke sent me last night. db=data.frame(year=1990:2016, ratio=c(.23,.27,.32,.37,.22,.26,.29,.15,.40,.28,.14,.09,.24,.18,.29,.51,.13,.17,.25,.13,.21,.29,.25,.2,.15,.12,.12)) In my previous post, I spend some ...

I had a very stranger discussion on twitter (yes, another one), about regression curves. I think it started with a tweet based on some xkcd picture (just for fun, because it was New Year’s Day) “don’t trust linear regressions” https://t.co/exUCvyRd1G pic.twitter.com/O6...

There are some graphs that you cannot forget. One graph that I found puzzling was mentioned on Andrew Gelman’s blog, a few years back, and was related to rupture detection What I remember from this graph is that if you want to get a rupture, you can easily find ...

A few months ago, I did mention that France was a country with strong inequalities, especially when you look at higher education, and research teams. Paris has almost 50% of the CNRS researchers, while only 3% of the population lives there. CNRS, "répartition des chercheurs en SHS" http://t.co/39dcJJBwrF, ...

Following my previous post, I wanted to use another dataset to visualize where people live, on Earth. The dataset is coming from sedac.ciesin.columbia.edu. We you register, you can download the database __ base=read.table("glp00ag15.asc",skip=6) The database is a ‘big’ 1440×572 matrix, in each cell (...

In our data science course, this morning, we’ve use random forrest to improve prediction on the German Credit Dataset. The dataset is __ url="http://freakonometrics.free.fr/german_credit.csv" __ credit=read.csv(url, header = TRUE, sep = ",") Almost all variables are treated a numeric, but actually, most of them ...

In our time series class this morning, I was discussing forecasts with ARIMA Models. Consider some simple stationnary AR(1) simulated time series __ n=95 __ set.seed(1) __ E=rnorm(n) __ X=rep(0,n) __ phi=.85 __ for(t in 2:n) X[t]=phi*X[t-1]+E[t] __ plot(X,type="l") If we fit ...