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...

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

This semester I'm teaching from Hastie, Tibshirani, and Friedman's book, The Elements of Statistical Learning, 2nd Edition. The authors provide a Mixture Simulation data set that has two continuous predictors and a binary outcome. This data is used to demonstrate classification procedures by plotting classification boundaries in the two predictors. For example, the figure below

D Kelly O’Day did a great post on charting NASA’s Goddard Institute for Space Studies (GISS) temperature anomaly data, but it sticks with base R for data munging & plotting. While there’s absolutely nothing wrong with base R operations, I thought a modern take on the chart using dplyr, magrittr & tidyr for data manipulation