...continuing our way though John Verzani's Using R for introductory statistics. Previous installments: chapt1&2, chapt3.1, chapt3.2 Relationships in numeric data If two data series have a natural pairing (x1,y1),...,(xn,yn), then we can ask, &ld...

A most interesting paper by Adrian Raftery and Le Bao appeared in the Early View section of Biometrics. It aims at better predictions for HIV prevalence—in the original UNAIDS implementation, a naïve SIR procedure was used, based on the prior as importance function, which sometimes resulted in terrible degeneracy—, but its methodological input is about

Back home after those two weeks in CiRM for our “research in pair” invitation to work on the new edition of Bayesian Core, I am very grateful for the support we received from CiRM and through it from SMF and CNRS. Being “locked” away in such a remote place brought a considerable increase in concentration

I see many economy indicator graphs that show emphasis by shading in the curve under the area (while x-axis is time). The shade is stronger at higher values (example). I did this in R below (ggplot2). This was a little more difficult that I’d expected. The color gradients are good to color each individual points

Having finally popped the stack on computing prime numbers with R in Part II and Part III, we are now in a position to discuss their relevance for computational scalability.My original intent was to show how poor partitioning of a workload can defeat the linear scalability expected when full parallelism is otherwise attainable, i.e., zero...

It is fairly straightforward to set the margins of a graph in R by calling the par() function with the mar (for margin!) argument. For example, par(mar=c(5.1,4.1,4.1,2.1) sets the bottom, left, top and right margins respectively of the plot region in number of lines of text. Another way is by specifying the margins in inches