is maps and geographical data representation in R. In case you’re curious too this is a good study material from R-Bloggers : maps ; geographical ; spatial Ok. This could be a tweet rather than a post…

R news and tutorials contributed by (552) R bloggers

is maps and geographical data representation in R. In case you’re curious too this is a good study material from R-Bloggers : maps ; geographical ; spatial Ok. This could be a tweet rather than a post…

There is a certain hype about mixed (and random) effects among statistician and analysts. You can show some love to Douglas Bates and Martin Maechler for maintaing the lme4 package for our cupid, R I copy the entity of the information of the projects page. Doxygen documentation of the underlying C functions is here. The

One of the greatest event on R is under way… R / Finance 2010: Applied Finance with R April 16 & 17, Chicago, IL, US The second annual R / Finance conference for applied finance using R, the premier free software system for statistical computation and graphics, will be held this spring in Chicago, IL, USA on Friday April 16

Well, I’m writing this from my new system. After years on hiatus I migrated to Linux, once again. Setting up a full system on Linux for a Greek user had been one of the greatest challenges. First,of all setting up writing, reading & printing in Greek was the biggest obstacle, I still recall memories of 2000/2001

Let’s say that you’re fitting a cumbersome model so time is not to waste over a PC staring at the screen half anxious-half bored… Then, you can always leave and go on with meetings and all your daily routine and have R notify you the results! How? We will illustrate the situation above using some Bayesian Model

Instead of the factor() function which usually applies after defining a vector there’s the gl() base function to do this in one step, egfreq <- c(204,6,1,211,13,5,357,44,38,92,34,49) row <- gl(4,3,length=12) col <- gl(3,1,length=12) > col 1 2 3 1 2 3 1 2 3 1 2 3 Levels: 1 2 3 tt <- data.frame(freq,row,col) > xtabs(tt) col row 1 2 3 1 204 6

Well, that’s a good book that you shouldn’t miss “Introduction to Applied Bayesian Statistics and Estimation for Social Scientists”. Why you shouldn’t miss it? Coz, it’s practical and I mean p r a c t i c a l big time!!! I don’t own tons of (traditionally) printed books but that’s one of the few breaking

…in R of course! There is a handy function to do those calculations. Normally (ahh!) you might resolve to a symbolic calculation package (Maple,Mathematica etc.) but that is not the situation any more. The calculations are done with the mnormt package. Relevant functions exist in other packages as well (R : Distributions)x <- seq(-2,4,length=21) y <- 2*x+10 z

Estimating a proportion at first looks elementary. Hail to aymptotics, right? Well, initially it might seem efficient to iuse the fact that . In other words the classical confidence interval relies on the inversion of Wald’s test. A function to ease the computation is the following (not really needed!).waldci<- function(x,n,level){ phat<-sum(x)/n results<-phat + c(-1,1)*qnorm(1-level/2)*sqrt(phat*(1-phat)/n) print(results) }An exact confidence interval is