by Joseph Rickert In a recent blog post, Revolution's Thomas Dinsmore announced stepwise regression for big data as a new feature of Revolution R Enterprise 6.2 that is scheduled for general availability later this month. Today, I would like to provide a simple example of doing stepwise regression with rxLinMod() (the RevoScaleR analog of lm()), using a 100,000 row...
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In case you missed it: March 2013 Roundup
In case you missed them, here are some articles from March of particular interest to R users. Facebook used R to analyze profile photo changes to create a map of same-sex marriage support in the USA. Joe Rickert contrasts random sampling with fitting models directly to large data sets. A presentation by Carlos Somohano summarizes the history, skills and...
Mastering Matrices
R has many ways to store information. Most of the time, our data comes in the form of a dataset, which we bring into R as a data.frame object. However, there are times when we want to use matrices as well. This post will show you how matrices can...
Worry about correctness and repeatability, not p-values
In data science work you often run into cryptic sentences like the following: Age adjusted death rates per 10,000 person years across incremental thirds of muscular strength were 38.9, 25.9, and 26.6 for all causes; 12.1, 7.6, and 6.6 for cardiovascular disease; and 6.1, 4.9, and 4.2 for cancer (all P < 0.01 for linear
An Introduction to SAS for R Programmers
by Joseph Rickert Life decisions are usually much too complicated to be attributed to any single cause, but one important reason that I am here at Revolution today is that I ignored suggestions from well-meaning faculty back in graduate school to work more in SAS rather than doing everything in R. There was a heavy emphasis on SAS then:...
a brief on naked statistics
Over the last Sunday breakfast I went through Naked Statistics: Stripping the Dread from the Data. The first two pages managed to put me in a prejudiced mood for the rest of the book. To wit: the author starts with some math bashing (like, no one ever bothers to tell us about the uses of 
What’s New in Release 6.2: Additional ScaleR Features
by Thomas Dinsmore Revolution R Enterprise Release 6.2 is in track for General Availability on April 22. In previous posts, I've commented on support for open source R 2.15.3 and Stepwise Regression. Today I'll wrap this series with a summary of some of the other new features supported in this release. Parallel Random Number Generation For analysts seeking to...
Estimating continuous piecewise linear regression
When talking about smoothing splines a simple point to start with is a continuous piecewise linear regression with fixed knots. I did not find any simple example showing how to estimate the it in GNU R so I have created a little snippet that does the j...
Introducing the healthvis R package – one line D3 graphics with R
We have been a little slow on the posting for the last couple of months here at Simply Stats. That’s bad news for the blog, but good news for our research programs! Today I’m announcing the new healthvis R package … Continue reading →
p-values are (possibly biased) estimates of the probability that the null hypothesis is true
Last week, I posted about statisticians’ constant battle against the belief that the p-value associated (for example) with a regression coefficient is equal to the probability that the null hypothesis is true, for a null hypothesis that beta is zero or negative. I argued that (despite our long pedagogical practice) there are, in fact, many 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
