Month: February 2011
Whats new in the latest version of R
CHANGES IN R VERSION 2.12.2: http://cran.r-project.org/src/base/NEWS SIGNIFICANT USER-VISIBLE CHANGES: • Complex arithmetic (notably z^n for complex z and integer n) gave incorrect results since R 2.10.0 on platforms without C99 complex support. This and some lesser issues in trignometric functions have been corrected. Such platforms were rare (we know of Cygwin and FreeBSD). However, because of new compiler optimizations in the way complex arguments are handled, the same code was selected on x86_64 Linux with gcc 4.5.x at the default -O2 optimization (but not at -O). • There is a workaround for crashes seen with several packages on systems using zlib 1.2.5: see the INSTALLATION section. NEW FEATURES: • PCRE has been updated to 8.12 (two bug-fix releases since 8.10). • rep(), seq(), seq.int() and seq_len() report more often when the first element is taken of an argument of incorrect length. • The Cocoa back-end for the quartz() graphics device on Mac OS X provides a way to disable event loop processing temporarily (useful, e.g., for forked instances of R). • kernel()'s default for m was not appropriate if coef was a set of coefficients. (Reported by Pierre Chausse.) • bug.report() has been updated for the current R bug tracker, which does not accept emailed submissions. • R CMD check now checks for the correct use of $(LAPACK_LIBS) (as well as $(BLAS_LIBS)), since several CRAN recent submissions have ignored ‘Writing R Extensions’. INSTALLATION: • The zlib sources in the distribution are now built with all symbols remapped: this is intended to avoid problems seen with packages such as XML and rggobi which link to zlib.so.1 on systems using zlib 1.2.5. • The default for FFLAGS and FCFLAGS with gfortran on x86_64 Linux has been changed back to -g -O2: however, setting -g -O may still be needed for gfortran 4.3.x. PACKAGE INSTALLATION: • A LazyDataCompression field in the DESCRIPTION file will be used to set the value for the --data-compress option of R CMD INSTALL. • Files R/sysdata.rda of more than 1Mb are now stored in the lazyload daabase using xz compression: this for example halves the installed size of package Imap. • R CMD INSTALL now ensures that directories installed from inst have search permission for everyone. It no longer installs files inst/doc/Rplots.ps and inst/doc/Rplots.pdf. These are almost certainly left-overs from Sweave runs, and are often large. DEPRECATED & DEFUNCT: • The ‘experimental’ alternative specification of a name space via .Export() etc is now deprecated. • zip.file.extract() is now deprecated. • Zip-ing data sets in packages (and hence R CMD INSTALL --use-zip-data and the ZipData: yes field in a DESCRIPTION file) is deprecated: using efficiently compressed .rda images and lazy-loading of data has superseded it. BUG FIXES: • identical() could in rare cases generate a warning about non-pairlist attributes on CHARSXPs. As these are used for internal purposes, the attribute check should be skipped. (Reported by Niels Richard Hansen). • If the filename extension (usually .Rnw) was not included in a call to Sweave(), source references would not work properly and the keep.source option failed. (PR#14459) • format.data.frame() now keeps zero character column names. • pretty(x) no longer raises an error when x contains solely non-finite values. (PR#14468) • The plot.TukeyHSD() function now uses a line width of 0.5 for its reference lines rather than lwd = 0 (which caused problems for some PDF and PostScript viewers). • The big.mark argument to prettyNum(), format(), etc. was inserted reversed if it was more than one character long. • R CMD check failed to check the filenames under man for Windows' reserved names. • The "Date" and "POSIXt" methods for seq() could overshoot when to was supplied and by was specified in months or years. • The internal method of untar() now restores hard links as file copies rather than symbolic links (which did not work for cross-directory links). • unzip() did not handle zip files which contained filepaths with two or more leading directories which were not in the zipfile and did not already exist. (It is unclear if such zipfiles are valid and the third-party C code used did not support them, but PR#14462 created one.) • combn(n, m) now behaves more regularly for the border case m = 0. (PR#14473) • The rendering of numbers in plotmath expressions (e.g. expression(10^2)) used the current settings for conversion to strings rather than setting the defaults, and so could be affected by what has been done before. (PR#14477) • The methods of napredict() and naresid() for na.action = na.exclude fits did not work correctly in the very rare event that every case had been omitted in the fit. (Reported by Simon Wood.) • weighted.residuals(drop0=TRUE) returned a vector when the residuals were a matrix (e.g. those of class "mlm"). (Reported by Bill Dunlap.) • Package HTML index files /html/00Index.html were generated with a stylesheet reference that was not correct for static browsing in libraries. • ccf(na.action = na.pass) was not implemented. • The parser accepted some incorrect numeric constants, e.g. 20x2. (Reported by Olaf Mersmann.) • format(*, zero.print) did not always replace the full zero parts. • Fixes for subsetting or subassignment of "raster" objects when not both i and j are specified. • R CMD INSTALL was not always respecting the ZipData: yes field of a DESCRIPTION file (although this is frequently incorrectly specified for packages with no data or which specify lazy-loading of data). R CMD INSTALL --use-zip-data was incorrectly implemented as --use-zipdata since R 2.9.0. • source(file, echo=TRUE) could fail if the file contained #line directives. It now recovers more gracefully, but may still display the wrong line if the directive gives incorrect information. • atan(1i) returned NaN+Infi (rather than 0+Infi) on platforms without C99 complex support. • library() failed to cache S4 metadata (unlike loadNamespace()) causing failures in S4-using packages without a namespace (e.g. those using reference classes). • The function qlogis(lp, log.p=TRUE) no longer prematurely overflows to Inf when exp(lp) is close to 1. • Updating S4 methods for a group generic function requires resetting the methods tables for the members of the group (patch contributed by Martin Morgan). • In some circumstances (including for package XML), R CMD INSTALL installed version-control directories from source packages. • Added PROTECT calls to some constructed expressions used in C level eval calls. • utils:::create.post() (used by bug.report() and help.request()) failed to quote arguments to the mailer, and so often failed. • bug.report() was naive about how to extract maintainer email addresses from package descriptions, so would often try mailing to incorrect addresses. • debugger() could fail to read the environment of a call to a function with a ... argument. (Reported by Charlie Roosen.) • prettyNum(c(1i, NA), drop0=TRUE) or str(NA_complex_) now work correctly.
Related Articles
- R 2.12.2 scheduled for February 25 (revolutionanalytics.com)
- Sweave Tutorial 3: Console Input and Output – Multiple Choice Test Analysis (r-bloggers.com)
Ohri's Johari Window
A Johari window is a cognitive psychological tool created by Joseph Luft and Harry Ingham in 1955[1] in the United States, used to help people better understand their interpersonal communication and relationships. It is used primarily in self-help groups and corporate settings as a heuristic exercise.
When performing the exercise, subjects are given a list of 56 adjectives and picks five or six that they feel describe their own personality. Peers of the subject are then given the same list, and each picks five or six adjectives that describe the subject. These adjectives are then mapped onto a grid
A Johari window consists of the following 56 adjectives used as possible descriptions of the participant. In alphabetical order they are:
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Ohri’s Johari Window
A Johari window is a cognitive psychological tool created by Joseph Luft and Harry Ingham in 1955[1] in the United States, used to help people better understand their interpersonal communication and relationships. It is used primarily in self-help groups and corporate settings as a heuristic exercise.
When performing the exercise, subjects are given a list of 56 adjectives and picks five or six that they feel describe their own personality. Peers of the subject are then given the same list, and each picks five or six adjectives that describe the subject. These adjectives are then mapped onto a grid
A Johari window consists of the following 56 adjectives used as possible descriptions of the participant. In alphabetical order they are:
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Julian Assange Dear Chap
Julian Assange a very Dear Chap
couldnt control his pecker
got caught in a honey trap
Should have kept that rubber on, Jules
Nordic Scandinavians may be easy but even they have rules
meanwhile Dear Chap’s Website the eponymous Wikileaks
is leaking revolution and democracy like Vegas casino magic tricks
The Arabs read his website before Sentor Joe crashed it down
And now Anglo Saxon allies in Egypy, Tunisia, Libya, Yemen, Bahrain are wearing a frown
Viva La Website Revolution Wikileaks
Merde to the Dear Chap\s pecker squeaks
Time up, time for all dictators to go and hide,
rulers Arabian, or Aussi hackers on a funny ride.
Related Articles
- Ten Things You Should Know Before Moving In With Julian Assange (blogs.forbes.com)
- Julian Assange Threatens To Sue The Guardian Again (huffingtonpost.com)
- “Swedish justice” part 1 – New editorial in Sweden’s biggest newspaper demands sentence for Julian Assange (tannhauser4.wordpress.com)
- Julian Assange Granted Bail in London at 240 ££ (rawjustice.com)
Zementis partners with R Analytics Vendor- Revo
Just got a PR email from Michael Zeller,CEO , Zementis annoucing Zementis (ADAPA) and Revolution Analytics just partnered up.
Is this something substantial or just time-sharing http://bi.cbronline.com/news/sas-ceo-says-cep-open-source-and-cloud-bi-have-limited-appeal or a Barney Partnership (http://www.dbms2.com/2008/05/08/database-blades-are-not-what-they-used-to-be/)
Summary- Thats cloud computing scoring of models on EC2 (Zementis) partnering with the actual modeling software in R (Revolution Analytics RevoDeployR)
See previous interviews with both Dr Zeller at https://decisionstats.com/2009/02/03/interview-michael-zeller-ceozementis/ ,https://decisionstats.com/2009/05/07/interview-ron-ramos-zementis/ and https://decisionstats.com/2009/10/05/interview-michael-zellerceo-zementis-on-pmml/)
and Revolution guys at https://decisionstats.com/2010/08/03/q-a-with-david-smith-revolution-analytics/
and https://decisionstats.com/2009/05/29/interview-david-smith-revolution-computing/
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strategic partnership with Revolution Analytics, the leading commercial provider of software and support for the popular open source R statistics language. With this partnership, predictive models developed on Revolution R Enterprise are now accessible for real-time scoring through the ADAPA Decisioning Engine by Zementis. ADAPA is an extremely fast and scalable predictive platform. Models deployed in ADAPA are automatically available for execution in real-time and batch-mode as Web Services. ADAPA allows Revolution R Enterprise to leverage the Predictive Model Markup Language (PMML) for better decision management. With PMML, models built in R can be used in a wide variety of real-world scenarios without requiring laborious or expensive proprietary processes to convert them into applications capable of running on an execution system.
“By partnering with Zementis, Revolution Analytics is building an end-to-end solution for moving enterprise-level predictive R models into the execution environment,” said Jeff Erhardt, Revolution Analytics Chief Operation Officer. “With Zementis, we are eliminating the need to take R applications apart and recode, retest and redeploy them in order to obtain desirable results.”
Got demo? Yes, we do! Revolution Analytics and Zementis have put together a demo which combines the building of models in R with automatic deployment and execution in ADAPA. It uses Revolution Analytics’ RevoDeployR, a new Web Services framework that allows for data analysts working in R to publish R scripts to a server-based installation of Revolution R Enterprise.
Action Items:
- Try our INTERACTIVE DEMO
- DOWNLOAD the white paper
- Try the ADAPA FREE TRIAL
RevoDeployR & ADAPA allow for real-time analysis and predictions from R to be effectively used by existing Excel spreadsheets, BI dashboards and Web-based applications, all in real-time.
Predictive analytics with RevoDeployR from Revolution Analytics and ADAPA from Zementis put model building and real-time scoring into a league of their own. Seriously!
Related Articles
- Revolution R Enterprise 4.2 now available (revolutionanalytics.com)
- Enterprise Startup Spotlight: Revolution Analytics, Taking on SAS, SPSS (readwriteweb.com)
- Gartner predicts business intelligence revolution (v3.co.uk)
Amazing Data Visualization- UN Counter Terrorism
Here is an amazing organigram depicting the organization of United Nations Task force of Countering Terrorism
source-
http://www.un.org/terrorism/cttaskforce.shtml
so now you know whom to call at 3 am, in case of airline bombing terrorists or just bad guys in general.