Blog Archives

What I need to know…

June 19, 2010
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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…

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lme4 stands 4 Linear mixed-effects…

February 19, 2010
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lme4 stands 4 Linear mixed-effects…

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

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[Event] R / Finance 2010: Applied Finance with R

February 5, 2010
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[Event] R / Finance 2010: Applied Finance with R

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

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Start your engines; it’s a Linux era!

December 29, 2009
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Start your engines; it’s a Linux era!

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

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R> if (done=TRUE) tweet me!

December 11, 2009
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R> if (done=TRUE) tweet me!

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

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Create factor variables in R

December 6, 2009
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Create factor variables in R

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  

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R twitts…

December 5, 2009
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R twitts…

You may already know this… #rstats I think I might get an account when get to connect to the Internet in my mobile phone

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[S. Lynch] Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

December 4, 2009
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[S. Lynch] Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

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

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Probability of hypercubes…

November 28, 2009
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Probability of hypercubes…

…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

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Confidence we seek…

November 18, 2009
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Confidence we seek…

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

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