If you’ve ever wondered how logistic population growth (the Verhulst model), S curves, the logistic map, bifurcation diagrams, sensitive dependence on initial conditions, “orbits”, deterministic chaos, and Lyapunov exponents are related to

“Master” R in Washington DC this September! Join RStudio Chief Data Scientist Hadley Wickham at the AMA – Executive Conference Center in Arlington, VA on September 14 and 15, 2015 for this rare opportunity to learn from one of the R community’s most popular and innovative authors and package developers. It will be at least another

Consider planning a clinicial trial where patients are randomized in permuted blocks of size four to either a 'control' or 'treatment' group. The outcome is measured on an 11-point ordinal scale (e.g., the numerical rating scale for pain). It may be reasonable to evaluate the results of this trial using a proportional odds cumulative logit

The 23rd iteration of the GIS Research UK conference (#GISRUK) conference was the largest ever. 250 researchers, industry representatives and academics attended from the vibrant geospatial research communities in the UK, Europe and beyond. GISRUK has become a centrepoint for discussion of new methods, software and applications in the field. I was on the organising committee, reviewed some excellent papers for the event (a full list...

You can find registration information and agenda details (as they become available) on the conference website. Or you can go directly to the registration page. Note that there's an early-bird registration deadl...

The annoucement below just went to the R-SIG-Finance list. More information is as usual at the R / Finance page. Registration for R/Finance 2015 is now open! The conference will take place on May 29 and 30, at UIC in Chicago. Building on the success of the previous conferences in 2009-2014, we expect more than 250 attendees from around...

In our data-science class, after discussing limitations of the logistic regression, e.g. the fact that the decision boundary line was a straight line, we’ve mentioned possible natural extensions. Let us consider our (now) standard dataset clr1 <- c(rgb(1,0,0,1),rgb(0,0,1,1)) clr2 <- c(rgb(1,0,0,.2),rgb(0,0,1,.2)) x <- c(.4,.55,.65,.9,.1,.35,.5,.15,.2,.85) y <- c(.85,.95,.8,.87,.5,.55,.5,.2,.1,.3) z <- c(1,1,1,1,1,0,0,1,0,0) df <- data.frame(x,y,z) plot(x,y,pch=19,cex=2,col=clr1) One can consider a quadratic...

We will start, in our Data Science course, to discuss classification techniques (in the context of supervised models). Consider the following case, with 10 points, and two classes (red and blue) > clr1 <- c(rgb(1,0,0,1),rgb(0,0,1,1)) > clr2 <- c(rgb(1,0,0,.2),rgb(0,0,1,.2)) > x <- c(.4,.55,.65,.9,.1,.35,.5,.15,.2,.85) > y <- c(.85,.95,.8,.87,.5,.55,.5,.2,.1,.3) > z <- c(1,1,1,1,1,0,0,1,0,0) > df <- data.frame(x,y,z) > plot(x,y,pch=19,cex=2,col=clr1) To get...

This post builds on a previous post, but can be read and understood independently. As part of my course on statistical learning, we created 3D graphics to foster a more intuitive understanding of the various methods that are used to relax the assumption of linearity (in the predictors) in regression and classification methods. The authors

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