Frank Harrell, chair of the Biostatistics department here at Vanderbilt, is giving a seminar entitled "Sweave for Reproducible Research and Beautiful Statistical Reports" tomorrow, Wednesday, May 12, 1:30-2:30pm, in the MRBIII Conference Room 1220. This tutorial covers the basics of Sweave and shows how to enhance the default output in various ways by using: latex methods for converting R...
R posts in other languages
- 适合初学者的R语言教程
- SAS hat einen neuen Vergleich von SAS und R veröffentlicht
- 实现可重复的统计slides
- О связи между “утечкой мозгов” и объемом пассажирских перевозок
- Новая книга по основам прогнозирования (с использованием R)
- Двухфакторный дисперсионный анализ
- Revolution R Enterprise 6.2 mit neuen Funktionen für hoch performante Analysen auf Big Data
- Third Milano R net meeting: leave a comment
- Photos of the third Milano R net meeting
- Presentations of the third Milano R net meeting
Number Formatting
I was discussing some subject with my kids - can't recall if it was in the realm of astronomy, computing, or moder economics. In any case, it involved large numbers. I fired up R to do a quick calculation:> 1000000000 / 1000The resulting answer was ...
R Package ‘rms’ for Regression Modeling
If you attended Frank Harrell's Regression Modeling Strategies course a few weeks ago, you got a chance to see the rms package for R in action. Frank's rms package does regression modeling, testing, estimation, validation, graphics, prediction, and ty...
Webinar May 20: Introduction to Revolution R
I'll be giving a live webinar on Thursday next week (May 20) titled Introduction to Revolution R. If you're new to the R world and wondering what you can do with R, this webinar is for you. I'll also be introducing some of the functionality unique to Revolution R included in our Revolution R Community (free to everyone) and...
R function names, explained
Why is the function to print out text in R named "cat"? Why is the function to delete objects called "rm"? Unless you have a background in Unix (or Linux) programming, some of R's command names can seem, well, a bit arcane. Jeromy Anglim explains the provenance of many of R's command names in this post: the details are...
Beware of rogue header files (Bioconductor installation)
Just a short note concerning a “gotcha”. As I have many times before, I opened an R console on my newly-upgraded (to lucid 10.04) Ubuntu machine, typed source(“http://bioconductor.org/biocLite.R”) and began a Bioconductor install with biocLite(). Only this time, I saw this: Error in dyn.load(file, DLLpath = DLLpath, ...) : unable to load shared library '/home/sau103/R/i486-pc-linux-gnu-library/2.11/affyio/libs/affyio.so': 
Putting Text in a Margin
I found text and title early on but was initially confounded when trying to add text outside of an actual chart. As it turns out, I needed to understand a bit about R's concept of margins in a chart> par(oma=c(2,2,2,2))> plot(rnorm)> mtext('The label'...
String Concatenation in R
String concatenation is a rather basic function - but my particular programming reflexes did not help me figure out how to do this in R. I tried the + and & operator, and even the || operator to no avail. Also tried concat() function... no dice. ...
A ridiculous email
Wolfram Research presumably has a robot that sends automated email following postings on arXiv: Your article, “Evidence and Evolution: A review”, caught the attention of one of my colleagues, who thought that it could be developed into an interesting Demonstration to add to the Wolfram Demonstrations Project. The Demonstrations Project, launched alongside Mathematica 6 in 
Example 7.36: Propensity score stratification
In examples 7.34 and 7.35 we described methods using propensity scores to account for possible confounding factors in an observational study.In addition to adjusting for the propensity score in a multiple regression and matching on the propensity score...
An economist explains: Why I use R
Economist and R blogger JD Long gave a talk last week (as part of the vconf.org project) about why he uses R to do statistical forecasts of agricultural yield for the reinsurance company he works for. I couldn't make the live session, but a replay is now available. The audio's a bit choppy, but if you've every struggled with...
ggplot2: Waterfall Charts
Waterfall charts are often used for analytical purposes in the business setting to show the effect of sequentially introduced negative and/or positive values. Sometimes waterfall charts are also referred to as cascade charts. In the next few paragraphs I will show how to plot a waterfall chart using ggplot2. Data A very small fictional dataset 
Abbreviations of R Commands Explained: 250+ R Abbreviations
The R programming language includes many abbreviations. Abbreviations exist in function names, argument names, and allowed values for arguments. This post expands on over 150 R abbreviations with the aim of making it easier for users new to R who are trying...
Abbreviations of R Commands Explained: 250+ R Abbreviations
The R programming language includes many abbreviations. Abbreviations exist in function names, argument names, and allowed values for arguments. This post expands on over 150 R abbreviations with the aim of making it easier for users new to R who are trying...
Tracking Long Term and Recent UAH Channel 5 Anomally Trends
In this post I present combined long term and recent trend charts of UAH Channel 5 temperature anomalies using R’s figure inside figure capability. This approach provides a better picture of global temperature trends than UAH’s day-of-y...
Book Review – Modern Applied Statistics with S by W. N. Venables and B. D. Ripley (Springer 2003)
Order this book from Amazon Modern Applied Statistics with S (Fourth Edition) is one of the oldest and most popular books on Applied Statistics using R and S-plus. A large number of topics in Applied Statistics are covered in this book and it is certainly not for the faint hearted. A sound knowledge of
Computational Statistics
Do not resort to Monte Carlo methods unnecessarily. When I received this 2009 Springer-Verlag book, Computational Statistics, by James Gentle a while ago, I briefly took a look at the table of contents and decided to have a better look later… Now that I have gone through the whole book, I can write a short 
Sweave with Emacs and ESS, problem solved!
With help from Seb, the nagging problem I had when trying to sweaving with Emacs and ESS (http://old.nabble.com/ESS-and-Sweave-td28339734.html) has been solved.First of all, apply the patch for ess-swv.el, provided by Seb. Second, set the default ...
Using the update function during variable selection
When fitting statistical models to data where there are multiple variables we are often interested in adding or removing terms from our model and in cases where there are a large number of terms it can be quicker to use the update function to start with a formula from a model that we have already
Forsythe’s algorithm
In connection with the Bernoulli factory post of last week, Richard Brent arXived a short historical note recalling George Forsythe’s algorithm for simulating variables with density when (the extension to any upper bound is straightforward). The idea is to avoid computing the exponential function by simulating uniforms until since the probability of this event is 
Basket Option Pricing: Step by Step
%7D%20=%20S_t%5E%7B(0)%7D%20r_t%20dt%20%5C%5C%20dS_t%5E%7B(1)%7D%20=%20S_t%5E%7B(1)%7D%20(%5Cmu_t%5E%7B(1)%7D%20dt%20+%20%5Csigma_t%5E%7B(1)%7D%20dW_t%5E%7B(1)%7D)%20%5C%5C%20dS_t%5E%7B(2)%7D%20=%20S_t%5E%7B(2)%7D%20(%5Cmu_t%5E%7B(2)%7D%20dt%20+%20%5Csigma_t%5E%7B(2)%7D%20dW_t%5E%7B(2)%7D)%20%5C%5C%20%5Ctext%7Bwhere%20%7D%20%20dW_t%5E%7B(1)%7DdW_t%5E%7B(2)%7D=%5Crho%20dt&bg=ffffff&%23038;fg=000000&%23038;s=0)
I find options fascinating because they deal with the abstract ideas of volatility and correlation, both of which are unobservable and can often seem like wild animal spirits (take the current stock market as an example). Understanding these subtle concepts is never easy, but it is essential in pricing some of the more exotic
Initial Post
As a web application and database developer who has spent most of my time in the world Java, Ruby, Python and SQL, the R language has some unfamiliar functionality.This blog will be comprised of tips and tricks for making charts with the R language.
Connecting R and Python
There are a few ways to do this, but the only one that worked for me was to use Rserve and rconnect. In R, do this: 1 2 3 install.packages("Rserve") library(Rserve) Rserve(debug = FALSE, port=6311, args=NULL) Then you can connect in Python very easily. Here is a test in Python: 1 2 rcmd = pyRserve.rconnect(host='localhost', port=6311) print(rcmd('rnorm(100)'))
Revolution’s 2010 roadmap
As part of all the news from yesterday, we also announced our vision and roadmap for the Revolution R product line for 2010. You can see a short summary of our vision in this two-minute video, or see more details in the roadmap whitepaper available for download. But here's a quick overview of our plans: First, we intend to...
Knowing whether a time-series has been differenced appropriately in order to make it stationary
Hello everybody,
Today I would like to make you learn a simple method (and of-course using R) how to identify whether a time-series has been differenced appropriately while making it stationary.
Suppose, you have made a series stationary by differencing it, now in order to know whether it is neither over nor under differenced subject the current series against next level...
Today I would like to make you learn a simple method (and of-course using R) how to identify whether a time-series has been differenced appropriately while making it stationary.
Suppose, you have made a series stationary by differencing it, now in order to know whether it is neither over nor under differenced subject the current series against next level...
Knowing whether a time-series has been differenced appropriately in order to make it stationary
Hello everybody,
Today I would like to make you learn a simple method (and of-course using R) how to identify whether a time-series has been differenced appropriately while making it stationary.
Suppose, you have made a series stationary by differencing it, now in order to know whether it is neither over nor under differenced subject the current series against next level...
Today I would like to make you learn a simple method (and of-course using R) how to identify whether a time-series has been differenced appropriately while making it stationary.
Suppose, you have made a series stationary by differencing it, now in order to know whether it is neither over nor under differenced subject the current series against next level...
A new site for the R community: inside-R.org
We've just launched a beta/preview of a new website for the R community, inside-R.org. The site is sponsored by Revolution Analytics (who funded its development and maintenance), but it's designed for anyone who uses or has in an interest in the R Project generally. So, you might ask, why another community site for R, when there's already several R...
Bayes vs. SAS
Glancing perchance at the back of my Amstat News, I was intrigued by the SAS advertisement Bayesian Methods Specify Bayesian analysis for ANOVA, logistic regression, Poisson regression, accelerated failure time models and Cox regression through the GENMOD, LIFEREG and PHREG procedures. Analyze a wider variety of models with the MCMC procedure, a general purpose Bayesian 
R: choose file dialog box
Needed this one recently, it pops up a window to pick a file to be used by r, then reads the contents into myData: myFile
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
