Jeroen Ooms writes: Here's a first version of a new web application for exploratory graphical analysis. It attempts to implement the layered graphics from the R package ggplot2 in a user-friendly way. This two-minute demo video demonstrates a ...

Jeroen Ooms writes: Here's a first version of a new web application for exploratory graphical analysis. It attempts to implement the layered graphics from the R package ggplot2 in a user-friendly way. This two-minute demo video demonstrates a ...

I finally pushed highlight to CRAN, which should be available in a few days. The package uses the information gathered by the parser package to perform syntax highlighting of R code The main function of the package is highlight, which takes a numb...

This article is quick concrete example of how to use the techniques from Survive R to lower the steepness of The R Project for Statistical Computing‘s learning curve (so an apology to all readers who are not interested in R). What follows is for people who already use R and want to achieve more control Related posts:

I've been working on this for quite some time (see here for a little background), so I'm pleased that it looks close to done at least as far as the core algorithm. It uses global variables for now, and I'm sure there are a couple of other bugs lurking, but here it is, after the jump.const.sqrt2pi <-...

Recently (2008) the European Space Agency produced GlobCover (ESA GlobCover Project, led by MEDIAS-France), the highest resolution (300m) global land cover map to date. GlobCover uses 21 primary land cover classes and many more sub-classes. Land cover classification (LCC) schemes divide the earth into biomes. Biomes are the simplest way to classify vegetation which can

In order to move some of my personal interests along, I have been trying to implement the methodology found in Berry and Berry's article Accounting for Multiplicities in Assessing Drug Safety. This methodology uses the MedDRA hierarchy to improve the p...

Prior to conducting an experiment researchers will often undertake power calculations to determine the sample size required in their work to detect a meaningful scientific effect with sufficient power. In R there are functions to calculate either a minimum sample size for a specific power for a test or the power of a test for

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

Just add a third column with link strength to the association matrix plotCophylo2 <- function (x, y, assoc = assoc, use.edge.length = use.edge.length, space = space, length.line = length.line, gap = gap, type = type, return = return, col = col, show.tip.label = show.tip.label, font = font) { if(ncol(assoc)==2) { assoc <- cbind(assoc,rep(1,nrow(assoc))) } res

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