An interesting announcemnet from Revolution Analytics promises to convert your legacy code in SAS language not only cheaper but faster. It’ s a very very interesting challenge and I wonder how SAS users ,corporates, customers as well as the Institute itself reacts
Are you paying for expensive software licenses and hardware to run time-consuming statistical analyses on big data sets?
If you’re doing linear regressions, logistic regressions, predictions, or multivariate crosstabulations* there’s something you should know: Revolution Analytics can get the same results for a substantially lower cost and faster than SAS®.
For a limited time only, Revolution Analytics invites you take the SAS to R Challenge. Let us prove that we can deliver on our promise of replicating your results in R, faster and cheaper than SAS.
Here’s how it works:
Fill out the short form below, and one of our conversion experts will contact you to discuss the SAS code you want to convert. If we think Revolution R Enterprise can get the same results faster than SAS, we’ll convert your code to R free of charge. Our goal is to demonstrate that Revolution R Enterprise will produce the same results in less time. There’s no obligation, but if you choose to convert, we guarantee that your license cost for Revolution R Enterprise will be less than half what you’re currently paying for the equivalent SAS software.**
It’s that simple.
We’ll show you that you don’t need expensive hardware and software to do high quality statistical analysis of big data. And we’ll show that you don’t need to tie up your computing resources with long running operations. With Revolution R Enterprise, you can run analyses on commodity hardware using Linux or Windows, scale to terabyte-class data problems and do it at processing speeds you would never have thought possible.
Sign up now, and we will be in touch shortly.
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SAS is a registered trademark of the SAS Institute, Cary, NC, in the US and other countries.
*Additional statistical algorithms are being rapidly added to Revolution R Enterprise. Custom development services are also available.
**Revolution Analytics retains the right to determine eligibility for this offer. Offer available until March 31, 2011.
R Commander Extensions: Enhancing a Statistical Graphical User Interface by extending menus to statistical packages
R Commander ( see paper by Prof J Fox at http://www.jstatsoft.org/v14/i09/paper ) is a well known and established graphical user interface to the R analytical environment.
While the original GUI was created for a basic statistics course, the enabling of extensions (or plug-ins http://www.r-project.org/doc/Rnews/Rnews_2007-3.pdf ) has greatly enhanced the possible use and scope of this software. Here we give a list of all known R Commander Plugins and their uses along with brief comments.
Note the naming convention for above e plugins is always with a Prefix of “RCmdrPlugin.” followed by the names above
Also on loading a Plugin, it must be already installed locally to be visible in R Commander’s list of load-plugin, and R Commander loads the e-plugin after restarting.Hence it is advisable to load all R Commander plugins in the beginning of the analysis session.
However the notable E Plugins are
1) DoE for Design of Experiments-
Full factorial designs, orthogonal main effects designs, regular and non-regular 2-level fractional
factorial designs, central composite and Box-Behnken designs, latin hypercube samples, and simple D-optimal designs can currently be generated from the GUI. Extensions to cover further latin hypercube designs as well as more advanced D-optimal designs (with blocking) are planned for the future.
2) Survival- This package provides an R Commander plug-in for the survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves, and testing for differences in survival curves, along with data-management facilities and a variety of tests, diagnostics and graphs.
3) qcc -GUI for Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts
4) epack- an Rcmdr “plug-in” based on the time series functions. Depends also on packages like , tseries, abind,MASS,xts,forecast. It covers Log-Exceptions garch
and following Models -Arima, garch, HoltWinters
5)Export- The package helps users to graphically export Rcmdr output to LaTeX or HTML code,
via xtable() or Hmisc::latex(). The plug-in was originally intended to facilitate exporting Rcmdr
output to formats other than ASCII text and to provide R novices with an easy-to-use,
easy-to-access reference on exporting R objects to formats suited for printed output. The
package documentation contains several pointers on creating reports, either by using
conventional word processors or LaTeX/LyX.
6) MAc- This is an R-Commander plug-in for the MAc package (Meta-Analysis with
Correlations). This package enables the user to conduct a meta-analysis in a menu-driven,
graphical user interface environment (e.g., SPSS), while having the full statistical capabilities of
R and the MAc package. The MAc package itself contains a variety of useful functions for
conducting a research synthesis with correlational data. One of the unique features of the MAc
package is in its integration of user-friendly functions to complete the majority of statistical steps
involved in a meta-analysis with correlations. It uses recommended procedures as described in
The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
A query to help for ??Rcmdrplugins reveals the following information which can be quite overwhelming given that almost 20 plugins are now available-
RcmdrPlugin.DoE::DoEGlossary
Glossary for DoE terminology as used in
RcmdrPlugin.DoE
RcmdrPlugin.DoE::Menu.linearModelDesign
RcmdrPlugin.DoE Linear Model Dialog for
experimental data
RcmdrPlugin.DoE::Menu.rsm
RcmdrPlugin.DoE response surface model Dialog
for experimental data
RcmdrPlugin.DoE::RcmdrPlugin.DoE-package
R-Commander plugin package that implements
design of experiments facilities from packages
DoE.base, FrF2 and DoE.wrapper into the
R-Commander
RcmdrPlugin.DoE::RcmdrPlugin.DoEUndocumentedFunctions
Functions used in menus
RcmdrPlugin.doex::ranblockAnova
Internal RcmdrPlugin.doex objects
RcmdrPlugin.doex::RcmdrPlugin.doex-package
Install the DOEX Rcmdr Plug-In
RcmdrPlugin.EHESsampling::OpenSampling1
Internal functions for menu system of
RcmdrPlugin.EHESsampling
RcmdrPlugin.EHESsampling::RcmdrPlugin.EHESsampling-package
Help with EHES sampling
RcmdrPlugin.Export::RcmdrPlugin.Export-package
Graphically export objects to LaTeX or HTML
RcmdrPlugin.FactoMineR::defmacro
Internal RcmdrPlugin.FactoMineR objects
RcmdrPlugin.FactoMineR::RcmdrPlugin.FactoMineR
Graphical User Interface for FactoMineR
RcmdrPlugin.IPSUR::IPSUR-package
An IPSUR Plugin for the R Commander
RcmdrPlugin.MAc::RcmdrPlugin.MAc-package
Meta-Analysis with Correlations (MAc) Rcmdr
Plug-in
RcmdrPlugin.MAd::RcmdrPlugin.MAd-package
Meta-Analysis with Mean Differences (MAd) Rcmdr
Plug-in
RcmdrPlugin.orloca::activeDataSetLocaP
RcmdrPlugin.orloca: A GUI for orloca-package
(internal functions)
RcmdrPlugin.orloca::RcmdrPlugin.orloca-package
RcmdrPlugin.orloca: A GUI for orloca-package
RcmdrPlugin.orloca::RcmdrPlugin.orloca.es
RcmdrPlugin.orloca.es: Una interfaz grafica
para el paquete orloca
RcmdrPlugin.qcc::RcmdrPlugin.qcc-package
Install the Demos Rcmdr Plug-In
RcmdrPlugin.qual::xbara
Internal RcmdrPlugin.qual objects
RcmdrPlugin.qual::RcmdrPlugin.qual-package
Install the quality Rcmdr Plug-In
RcmdrPlugin.SensoMineR::defmacro
Internal RcmdrPlugin.SensoMineR objects
RcmdrPlugin.SensoMineR::RcmdrPlugin.SensoMineR
Graphical User Interface for SensoMineR
RcmdrPlugin.SLC::Rcmdr.help.RcmdrPlugin.SLC
RcmdrPlugin.SLC: A GUI for slc-package
(internal functions)
RcmdrPlugin.SLC::RcmdrPlugin.SLC-package
RcmdrPlugin.SLC: A GUI for SLC R package
RcmdrPlugin.sos::RcmdrPlugin.sos-package
Efficiently search R Help pages
RcmdrPlugin.steepness::Rcmdr.help.RcmdrPlugin.steepness
RcmdrPlugin.steepness: A GUI for
steepness-package (internal functions)
RcmdrPlugin.steepness::RcmdrPlugin.steepness
RcmdrPlugin.steepness: A GUI for steepness R
package
RcmdrPlugin.survival::allVarsClusters
Internal RcmdrPlugin.survival Objects
RcmdrPlugin.survival::RcmdrPlugin.survival-package
Rcmdr Plug-In Package for the survival Package
RcmdrPlugin.TeachingDemos::RcmdrPlugin.TeachingDemos-package
Install the Demos Rcmdr Plug-In
With programs like Computer Science for High School (CS4HS), we hope to increase the number of CS majors —and therefore the number of people entering into careers in CS—by promoting computer science curriculum at the high school level.
For the fourth consecutive year, we’re funding CS4HS to invest in the next generation of computer scientists and engineers. CS4HS is a workshop for high school and middle school computer science teachers that introduces new and emerging concepts in computing and provides tips, tools and guidance on how to teach them. The ultimate goals are to “train the trainer,” develop a thriving community of high school CS teachers and spread the word about the awe and beauty of computing.
If you’re a university, community college, or technical School in the U.S., Canada, Europe, Middle East or Africa and are interested in hosting a workshop at your institution, please visit www.cs4hs.com to submit an application for grant funding.Applications will be accepted between January 18, 2011 and February 18, 2011.
In addition to submitting your application, on the CS4HS website you’ll find info on how to organize a workshop, as well as websites and agendas from last year’s participants to give you an idea of how the workshops were structured in the past. There’s also a collection ofCS4HS curriculum modules that previous participating schools have shared for future organizers to use in their own program.
Related Articles (Ps the Related Articles is auto generated by Zementa- a software embedded within WordPress.com in case you are wondering what the deal with the linking is)
R Authors get more choice and variety now-
http://www.mail-archive.com/r-help@r-project.org/msg122965.html
We are pleased to announce the launch of a new series of books on R.
Chapman & Hall/CRC: The R Series
Aims and Scope
This book series reflects the recent rapid growth in the development and
application of R, the programming language and software environment for
statistical computing and graphics. R is now widely used in academic research,
education, and industry. It is constantly growing, with new versions of the
core software released regularly and more than 2,600 packages available. It is
difficult for the documentation to keep pace with the expansion of the
software, and this vital book series provides a forum for the publication of
books covering many aspects of the development and application of R.
The scope of the series is wide, covering three main threads:
• Applications of R to specific disciplines such as biology, epidemiology,
genetics, engineering, finance, and the social sciences.
• Using R for the study of topics of statistical methodology, such as linear
and mixed modeling, time series, Bayesian methods, and missing data.
• The development of R, including programming, building packages, and graphics.
The books will appeal to programmers and developers of R software, as well as
applied statisticians and data analysts in many fields. The books will feature
detailed worked examples and R code fully integrated into the text, ensuring
their usefulness to researchers, practitioners and students.
Series Editors
John M. Chambers (Department of Statistics, Stanford University, USA;
j...@stat.stanford.edu)
Torsten Hothorn (Institut für Statistik, Ludwig-Maximilians-Universität,
München, Germany; torsten.hoth...@stat.uni-muenchen.de)
Duncan Temple Lang (Department of Statistics, University of California, Davis,
USA; dun...@wald.ucdavis.edu)
Hadley Wickham (Department of Statistics, Rice University, Houston, Texas, USA;
had...@rice.edu)
Call for Proposals
We are interested in books covering all aspects of the development and
application of R software. If you have an idea for a book, please contact one
of the series editors above or one of the Chapman & Hall/CRC statistics
acquisitions editors below. Please provide brief details of topic, audience,
aims and scope, and include an outline if possible.
We look forward to hearing from you.
Best regards,Rob Calver (rob.cal...@informa.com)
David Grubbs (david.gru...@taylorandfrancis.com)
John Kimmel (john.kim...@taylorandfrancis.com)
This promotional offer enables you to try a limited amount of the Windows Azure platform at no charge. The subscription includes a base level of monthly compute hours, storage, data transfers, a SQL Azure database, Access Control transactions and Service Bus connections at no charge. Please note that any usage over this introductory base level will be charged at standard rates.
Included each month at no charge:
Windows Azure
25 hours of a small compute instance
500 MB of storage
10,000 storage transactions
SQL Azure
1GB Web Edition database (available for first 3 months only)
Windows Azure platform AppFabric
100,000 Access Control transactions
2 Service Bus connections
Data Transfers (per region)
500 MB in
500 MB out
Any monthly usage in excess of the above amounts will be charged at the standard rates. This introductory special will end on March 31, 2011 and all usage will then be charged at the standard rates.
As part of AWS’s Free Usage Tier, new AWS customers can get started with Amazon EC2 for free. Upon sign-up, new AWScustomers receive the following EC2 services each month for one year:
750 hours of EC2 running Linux/Unix Micro instance usage
750 hours of Elastic Load Balancing plus 15 GB data processing
10 GB of Amazon Elastic Block Storage (EBS) plus 1 million IOs, 1 GB snapshot storage, 10,000 snapshot Get Requests and 1,000 snapshot Put Requests
15 GB of bandwidth in and 15 GB of bandwidth out aggregated across all AWS services
Paid Instances-
Standard On-Demand Instances
Linux/UNIX Usage
Windows Usage
Small (Default)
$0.085 per hour
$0.12 per hour
Large
$0.34 per hour
$0.48 per hour
Extra Large
$0.68 per hour
$0.96 per hour
Micro On-Demand Instances
Micro
$0.02 per hour
$0.03 per hour
High-Memory On-Demand Instances
Extra Large
$0.50 per hour
$0.62 per hour
Double Extra Large
$1.00 per hour
$1.24 per hour
Quadruple Extra Large
$2.00 per hour
$2.48 per hour
High-CPU On-Demand Instances
Medium
$0.17 per hour
$0.29 per hour
Extra Large
$0.68 per hour
$1.16 per hour
Cluster Compute Instances
Quadruple Extra Large
$1.60 per hour
N/A*
Cluster GPU Instances
Quadruple Extra Large
$2.10 per hour
N/A*
* Windows is not currently available for Cluster Compute or Cluster GPU Instances.
NOTE- Amazon Instance definitions differ slightly from Azure definitions
Instances of this family are well suited for most applications.
Small Instance – default*
1.7 GB memory
1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit)
160 GB instance storage
32-bit platform
I/O Performance: Moderate
API name: m1.small
Large Instance
7.5 GB memory
4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each)
850 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.large
Extra Large Instance
15 GB memory
8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each)
1,690 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.xlarge
Micro Instances
Instances of this family provide a small amount of consistent CPU resources and allow you to burst CPU capacity when additional cycles are available. They are well suited for lower throughput applications and web sites that consume significant compute cycles periodically.
Micro Instance
613 MB memory
Up to 2 EC2 Compute Units (for short periodic bursts)
EBS storage only
32-bit or 64-bit platform
I/O Performance: Low
API name: t1.micro
High-Memory Instances
Instances of this family offer large memory sizes for high throughput applications, including database and memory caching applications.
High-Memory Extra Large Instance
17.1 GB of memory
6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each)
420 GB of instance storage
64-bit platform
I/O Performance: Moderate
API name: m2.xlarge
High-Memory Double Extra Large Instance
34.2 GB of memory
13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each)
850 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.2xlarge
High-Memory Quadruple Extra Large Instance
68.4 GB of memory
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.4xlarge
High-CPU Instances
Instances of this family have proportionally more CPU resources than memory (RAM) and are well suited for compute-intensive applications.
High-CPU Medium Instance
1.7 GB of memory
5 EC2 Compute Units (2 virtual cores with 2.5 EC2 Compute Units each)
350 GB of instance storage
32-bit platform
I/O Performance: Moderate
API name: c1.medium
High-CPU Extra Large Instance
7 GB of memory
20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: c1.xlarge
Cluster Compute Instances
Instances of this family provide proportionally high CPU resources with increased network performance and are well suited for High Performance Compute (HPC) applications and other demanding network-bound applications. Learn more about use of this instance type for HPC applications.
Cluster Compute Quadruple Extra Large Instance
23 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc1.4xlarge
Cluster GPU Instances
Instances of this family provide general-purpose graphics processing units (GPUs) with proportionally high CPU and increased network performance for applications benefitting from highly parallelized processing, including HPC, rendering and media processing applications. While Cluster Compute Instances provide the ability to create clusters of instances connected by a low latency, high throughput network, Cluster GPU Instances provide an additional option for applications that can benefit from the efficiency gains of the parallel computing power of GPUs over what can be achieved with traditional processors. Learn moreabout use of this instance type for HPC applications.
Cluster GPU Quadruple Extra Large Instance
22 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
2 x NVIDIA Tesla “Fermi” M2050 GPUs
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cg1.4xlarge
versus-
Windows Azure compute instances come in five unique sizes to enable complex applications and workloads.
Compute Instance Size
CPU
Memory
Instance Storage
I/O Performance
Extra Small
1 GHz
768 MB
20 GB*
Low
Small
1.6 GHz
1.75 GB
225 GB
Moderate
Medium
2 x 1.6 GHz
3.5 GB
490 GB
High
Large
4 x 1.6 GHz
7 GB
1,000 GB
High
Extra large
8 x 1.6 GHz
14 GB
2,040 GB
High
*There is a limitation on the Virtual Hard Drive (VHD) size if you are deploying a Virtual Machine role on an extra small instance. The VHD can only be up to 15 GB.
Please use the following code to get a 15% discount on the 2 Day Conference Pass: AJAY11.
Predictive Analytics World announces new full-day workshops coming to San Francisco March 13-19, amounting to seven consecutive days of content.
These workshops deliver top-notch analytical and business expertise across the hottest topics.
Register now for one or more workshops, offered just before and after the full two-day Predictive Analytics World conference program (March 14-15). Early Bird registration ends on January 31st – take advantage of reduced pricing before then.
Make savings now with the early bird rate. Receive $200 off your registration rate for Predictive Analytics World – San Francisco (March 14-15), plus $100 off each workshop for which you register.
DECISION STATS 