This morning, Stéphane asked me tricky question about extracting coefficients from a regression with categorical explanatory variates. More precisely, he asked me if it was possible to store the coefficients in a nice table, with information on the variable and the modality (those two information being in two different columns). Here is some code I did to produce the...
It's been some time since my last post on football. And we're talking about european soccer here.So I finally managed to write some functions which allow me to extract player stats from www.transfermarkt.de. The site tracks lots of stats in the world o...
In my last post I showed how one can easily summarize the outcome of a logistic regression. Here I want to show how this really depends on the data-points that are used to estimate the model. Taking a cue from the evolution of a correlation I have plotted the estimated Odds Ratios (ORs) depending on
the survey of consumer finances (scf) tracks the wealth of american families. every three years, more than five thousand households answer a battery of questions about income, net worth, credit card debt, pensions, mortgages, even the lease on th...
In part I, we looked at importing data into R and simple ways to manipulate data frames. Once we’ve gotten our data safely into R, the first thing we want to do is probably to make some plots. Below, we’ll make some simple plots of the made-up comb gnome data. If you want to play 
In order to illustrate the construction of regression tree (using the CART methodology), consider the following simulated dataset, > set.seed(1) > n=200 > X1=runif(n) > X2=runif(n) > P=.8*(X1<.3)*(X2<.5)+ + .2*(X1<.3)*(X2>.5)+ + .8*(X1>.3)*(X1<.85)*(X2<.3)+ + .2*(X1>.3)*(X1<.85)*(X2>.3)+ + .8*(X1>.85)*(X2<.7)+ + .2*(X1>.85)*(X2>.7) > Y=rbinom(n,size=1,P) > B=data.frame(Y,X1,X2) with one dichotomos varible (the variable of interest, ), and two continuous ones (the explanatory ones and ). > tail(B) Y...
Having posted some code to Git a few days ago and having been wholly dissatisfied with it, I began to do what I often do with code I don’t like. I started re-writing it bigger and weirder and more philosophically pure. Part of this search for Platonic code lead me to explore object oriented programming 
At least fourfive R packages will turn your regression models into pretty latex tables: texreg, xtable, apsrtable, memisc, and stargazer. This is very nice if you happen to be a latex document or its final reader, but it’s not so great if you’re making those models to start with. What if you wanted to see
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
