Montreal R Users Group See the general information page for workshop goals, format, what to expect, and what you need. You can reserve your spot by signing-up to the Montreal R User Group and registering for the workshops you are interested in. The workshops are free to attend (!) We are now supported by: We can be found on Statistics for a changing world: Google Public Data Explorer in Labs Last year, we released a public data search feature that enables people to quickly find useful statistics in search. More recently, we expanded this service to include information from the World Bank, such as population data for every region in the world. More and more public agencies, non-profits and other organizations are looking for ways to open up their data and expand global access to this kind of information. We want to help keep that momentum going, so today we're sharing a snapshot of some of the most popular public data search topics on Google. Popular public data topics on GoogleWe know people want to be able to find reliable data and statistics on a variety of subjects. You can read the complete list at this link (PDF), but here's the top 20 to get you started: You'll notice some interesting entries in the list. This chart compares life expectancy and the number of births per woman over the last 47 years for most economies of the world.
Romain Francois, Professional R Enthusiast Amateur Mapmaking: Getting Started With Shapefiles One of the great things about (software) code is that people build on it and out from it… Which means that as well as producing ever more complex bits of software, tools also get produced over time that make it easier to do things that were once hard to do, or required expensive commercial software tools. Producing maps is a fine example of this. Not so very long ago, producing your own annotated maps was a hard thing to do. Then in June, 2005, or so, the Google Maps API came along and suddenly you could create your own maps (or at least, put markers on to a map if you had latitude and longitude co-ordinates available). Since then, things have just got easier. Sometimes, though, when using maps to visualise numerical data sets, just putting markers onto a map, even when they are symbols sized proportionally in accordance with your data, doesn’t quite achieve the effect you want. But what do you do if the boundaries/shapes you want to plot aren’t supported by the OS API?
Rattle: A Graphical User Interface for Data Mining using R Social Science Statistics Blog 28 April 2013 App Stats: Roberts, Stewart, and Tingley on "Topic models for open ended survey responses with applications to experiments" We hope you can join us this Wednesday, May 1, 2013 for the Applied Statistics Workshop. Molly Roberts, Brandon Stewart, and Dustin Tingley, all from the Department of Government at Harvard University, will give a presentation entitled "Topic models for open ended survey responses with applications to experiments". A light lunch will be served at 12 pm and the talk will begin at 12.15. "Topic models for open ended survey responses with applications to experiments" Molly Roberts, Brandon Stewart, and Dustin Tingley Government Department, Harvard University CGIS K354 (1737 Cambridge St.) Abstract: Despite broad use of surveys and survey experiments by political science, the vast majority of survey analysis deals with responses to options along a scale or from pre-established categories. Posted by Konstantin Kashin at 11:25 PM | Comments (2) 22 April 2013
StatsRUs Paul E. Johnson <pauljohn @ ku.edu> The original Rtips started in 1999. It became difficult to update because of limitations in the software with which it was created. Now I know more about R, and have decided to wade in again. In January, 2012, I took the FaqManager HTML output and converted it to LaTeX with the excellent open source program pandoc, and from there I’ve been editing and updating it in LyX. You are reading the New Thing! The first chore is to cut out the old useless stuff that was no good to start with, correct mistakes in translation (the quotation mark translations are particularly dangerous, but also there is trouble with ~, $, and -. (I thought it was cute to call this “StatsRus” but the Toystore’s lawyer called and, well, you know…) If you need a tip sheet for R, here it is. This is not a substitute for R documentation, just a list of things I had trouble remembering when switching from SAS to R. Heed the words of Brian D. 1.1 Bring raw numbers into R (05/22/2012) Step 1.
knitr: Elegant, flexible and fast dynamic report generation with R | knitr Overview The knitr package was designed to be a transparent engine for dynamic report generation with R, solve some long-standing problems in Sweave, and combine features in other add-on packages into one package (knitr ≈ Sweave + cacheSweave + pgfSweave + weaver + animation::saveLatex + R2HTML::RweaveHTML + highlight::HighlightWeaveLatex + 0.2 * brew + 0.1 * SweaveListingUtils + more). This package is developed on GitHub; for installation instructions and FAQ's, see README. Motivation One of the difficulties with extending Sweave is we have to copy a large amount of code from the utils package (the file SweaveDrivers.R has more than 700 lines of R code), and this is what the two packages mentioned above have done. Let us change our traditional attitude to the construction of programs: Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to humans what we want the computer to do. -- Donald E. Features Acknowledgements Misc
The Endeavour — The blog of John D. Cook I help people make decisions in the face of uncertainty. Sounds interesting. I’m a data scientist. I study machine learning. I’m into big data. Even though each of these descriptions makes a different impression, they’re all essentially the same thing. There are distinctions. “Decision-making under uncertainty” emphasizes that you never have complete data, and yet you need to make decisions anyway. “Data science” stresses that there is more to the process of making inferences than what falls under the traditional heading of “statistics.” Despite the hype around the term data science, it’s growing on me. Machine learning, like decision theory, emphasizes the ultimate goal of doing something with data rather than creating an accurate model of the process that generates the data. “Big data” is a big can of worms. The term “statistics” literally means the mathematics of the interests of states, as in governments, because these were the first applications of statistics.
Romain Francois, Professional R Enthusiast Sunday, March 24 2013 Moving By romain francois on Sunday, March 24 2013, 16:52 This blog is moving to blog.r-enthusiasts.com. The new one is powered by wordpress and gets a subdomain of r-enthusiasts.com. See you there no trackback Monday, February 18 2013 Improving the graph gallery By romain francois on Monday, February 18 2013, 08:37 I'm trying to make improvements to the R Graph Gallery, I'm looking for suggestions from users of the website. I've started a question on the website's facebook page. no trackback Monday, February 4 2013 bibtex 0.3-5 By romain francois on Monday, February 4 2013, 11:59 - bibtex The version 0.3-5 of the bibtex package is on CRAN. no trackback Monday, November 5 2012 OOP with Rcpp modules By romain francois on Monday, November 5 2012, 16:24 - Rcpp The purpose of Rcpp modules has always been to make it easy to expose C++ functions and classes to R. The classes might look like this: And we can expose these classes to R using the following module declarative code: no trackback
Making Reproducible Research Enjoyable | Yihui Xie Note: this is a contributed article for the ICSA Bulletin and the basic idea can be summarized in this picture. It is hard to convince people to think about reproducible research (RR). There are two parts of difficulties: (1) tools used to be for experts only and (2) it is still common practice to copy and paste. For some statisticians, RR is almost equivalent to Sweave (R + LaTeX). But RR is really important. The basic idea was the same: to mix code and text together, then compile the whole document with code being executed, and you get a report without copying/pasting anything since the code will faithfully give you results. I use Github extensively and learned markdown there. Later RStudio (the IDE of R) saw the work of knitr and decided to add support to it. After the Sweave work was done, I suggested markdown to RStudio developers, and fortunately they listened. And suddenly a golden glow descended on me, and all my sins were washed away...
Fishing in the Bay » Blog Archive » Why I am in favour of logging A colleague recently brought to me some alternative fits he had done for a paper he was writing. The alternative fits looked very strange but had been strongly suggested by a referee. He was fitting a regression model to inter-country trade data and trying to explain patterns in terms of various measures of cultural fit. The referee was pointing to some papers in econometrics that had argued about the relative merits of multiplicative regression models fitted on the direct scale, rather than on the log-scale. The referee wanted a direct fit on the basis that the random errors may be more normal and additive on the direct scale. One of the papers he was pointing to is HERE which contains the unequivocal recommendation Overall, except under very special circumstances, estimation based on the log-linear model cannot be recommended. Sounds like complete bollocks to me. Why is the log-transform better? Leverage effects can be huge on the direct scale. So there you have it.
CRANberries Package rms updated to version 4.2-0 with previous version 4.1-3 dated 2014-03-02 Title: Regression Modeling Strategies Description: Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. rms is a collection of functions that assist with and streamline modeling. Diff between rms versions 4.1-3 dated 2014-03-02 and 4.2-0 dated 2014-04-13 More information about rms at CRAN Permanent link