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 Blog Revolution - statistics by Terry M. Therneau Ph.D.Faculty, Mayo Clinic About a year ago there was a query about how to do "type 3" tests for a Cox model on the R help list, which someone wanted because SAS does it. The SAS addition looked suspicious to me, but as the author of the survival package I thought I should understand the issue more deeply. First off, what exactly is this 'type 3' computation of which SAS so deeply enamored? To compute this, one can start by fitting a sufficiently rich model, get predicted values for our age distribution, and then average them. A primary statistical issue with type 3 is the choice of reference. Average population prediction also highlights a serious deficiency in R. R needs a general and well thought out post-fit contrasts function. Also, I need to mention a couple more things: The standard methods for computing type 3 that I see in the help lists are flawed, giving seriously incorrect answers unless sum-to-zero constraints were used for the fit (contr.sum).
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
[FRENCH] Data Mining avec R dans un monde libre R est un langage et un environnement pour les calculs statistiques et leurs représentations graphiques. R est similaire au système S qui a été récompensé par le Software System Award de l'ACM (Association for Computing Machinery) et qui est la plate-forme du logiciel commercial S-Plus. Rappelons quelques technologies reconnues par la récompense ACM à savoir UNIX, TeX, PostScript, TCP/IP, World-Wide Web, Tcl/Tk, et Apache. Le but de l'article précédemment paru dans le FI 2/01 [1] était de fournir un point de départ pour les novices intéressés par R [2]. Le présent article se propose d'illustrer les interfaces existantes entre R et des bases de données relationnelles, ces interfaces étant un premier pas des logiciels statistiques modernes, comme R, vers la reconquête par les statisticiens du domaine du Knowledge Discovery in Databases (KDD). Qu'est-ce que le data mining? «We are drowning in information, but starving for knowledge.» Les bases de données et SQL On aura l'affichage:
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.
Romain Francois, Professional R Enthusiast R-statistics blog One R Tip A Day