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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.
- DoE – http://cran.r-project.org/web/packages/RcmdrPlugin.DoE/RcmdrPlugin.DoE.pdf
- epack- http://cran.r-project.org/web/packages/RcmdrPlugin.epack/RcmdrPlugin.epack.pdf
- Export- http://cran.r-project.org/web/packages/RcmdrPlugin.Export/RcmdrPlugin.Export.pdf
- MAc- http://cran.r-project.org/web/packages/RcmdrPlugin.MAc/RcmdrPlugin.MAc.pdf
- qcc- http://cran.r-project.org/web/packages/RcmdrPlugin.qcc/RcmdrPlugin.qcc.pdf and http://cran.r-project.org/web/packages/qcc/qcc.pdf
- Teaching Demos
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-
Glossary for DoE terminology as used in
RcmdrPlugin.DoE Linear Model Dialog for
RcmdrPlugin.DoE response surface model Dialog
for experimental data
R-Commander plugin package that implements
design of experiments facilities from packages
DoE.base, FrF2 and DoE.wrapper into the
Functions used in menus
Internal RcmdrPlugin.doex objects
Install the DOEX Rcmdr Plug-In
Internal functions for menu system of
Help with EHES sampling
Graphically export objects to LaTeX or HTML
Internal RcmdrPlugin.FactoMineR objects
Graphical User Interface for FactoMineR
An IPSUR Plugin for the R Commander
Meta-Analysis with Correlations (MAc) Rcmdr
Meta-Analysis with Mean Differences (MAd) Rcmdr
RcmdrPlugin.orloca: A GUI for orloca-package
RcmdrPlugin.orloca: A GUI for orloca-package
RcmdrPlugin.orloca.es: Una interfaz grafica
para el paquete orloca
Install the Demos Rcmdr Plug-In
Internal RcmdrPlugin.qual objects
Install the quality Rcmdr Plug-In
Internal RcmdrPlugin.SensoMineR objects
Graphical User Interface for SensoMineR
RcmdrPlugin.SLC: A GUI for slc-package
RcmdrPlugin.SLC: A GUI for SLC R package
Efficiently search R Help pages
RcmdrPlugin.steepness: A GUI for
steepness-package (internal functions)
RcmdrPlugin.steepness: A GUI for steepness R
Internal RcmdrPlugin.survival Objects
Rcmdr Plug-In Package for the survival Package
Install the Demos Rcmdr Plug-In
- New edition of “R Companion to Applied Regression” – by John Fox and Sandy Weisberg (r-bloggers.com)
- Reasons for Transitioning to Vim: Bringing LaTeX, R, Sweave and More under One Roof (r-bloggers.com)
Occam’s razor (or Ockham’s razor) is often expressed in Latin as the lex parsimoniae(translating to the law of parsimony, law of economy or law of succinctness). The principle is popularly summarized as “the simplest explanation is more likely the correct one.
Using a simple screenshot- you can see Facebook Analytics for a Facebook page is simpler at explaining who is coming to visit rather than Google Analytics Dashboard (which has not seen the attention of a Visual UI or Graphic Redesign)
And if Facebook is going to take over the internet, well it is definitely giving better analytics in the process. What do you think?
Which Interface is simpler- and gives you better targeting. Ignore the numbers and just see the metrics measured and the way they are presented. Coincidently R is used at Facebook a lot (which has given the jjplot package)- and Google has NOT INVESTED MAJOR MONEY in creating Premium R Packages or Big Data Packages. I am talking investment at the scale Google is known for- not measly meetups.
(the summer of code dont count- it is for students mostly)
(but thanks for the Pizza G Men- and maybe revise that GA interface by putting a razor to some metrics)
GA vs Facebook Analytics
- The Deal with the Devil (politics.ie)
- Google Analytics: Goodbye Site Overlay, Hello In-Page Analytics (searchengineland.com)
- Google Instant Page Previews Wreaking Havoc On Web Analytics (seroundtable.com)
- Designing Your Blog with User Interface in Mind (gabrielcatalano.com)
An interview with a noted Indian Software CEO, mentions China the possible biggest threat in next 5 years at http://www.thehindubusinessline.com/2010/10/13/stories/2010101353180700.htm
“I believe it (China) is the biggest threat in the next five years that we are going to face…So India will have to up its game,” he told reporters on sidelines of ‘Directions’, the company’s annual town hall.
Terming China, as both “threat and opportunity”, Mr Nayar said that India will have to find alternate “differentiators” than the ones it currently has. Despite issues of language and the purported inability to scale-up, China has sharpened its technological and innovation edge, he added.
“Look at the technology companies from China…how does that fit in with the assumption that they (China) do not understand English or technology. They are producing cutting edge technology at a price which is lower than everyone else,” he said.
By 2015, Mr Nayar said, China will be the lowest cost manpower supplier in IT sector to the world
I wonder how he did his forecast. Did he do a time series analysis using a software, did he peer into his crystal ball, or did he spend a lot of time brainstorming with his strategic macro economic team on Chinese threat.
China has various advantages over India (and in fact the US)-
1) Big pool of reliable scientific manpower
2) State funded education in higher studies and STEM
3) Increasing exposure with the West-English speaking is no longer an issue. Almost 50 % of Grad Students in the US in STEM and certain sectors are Chinese and they not only retain fraternal ties with the motherland- they often remain un-assimilated with American Culture mainstream. or they have a separate interaction with fellow American Chinese and seperate with American Americans.
Chinese suffer from some disadvantages in software-
1) Communism Perception- Just because the Govt is communist and likes to confront US once a year (and India twice a month)- is no excuse for the hapless Chinese startup guy to lose out on software outsourcing contracts. unfortunately there have been reported cases where sneak codes have been inserted in code deliverables for American partners, just like American companies are forced to work with DoD (especially in software, embedded chips and telecom)
If you have 10000 lines of code delivered by your Chinese partner, how sure are you of going through each line of code for each sub routine or call procedure.
2) English- Chinese accent is like Chinese cooking. Unique- many Chinese are unable to master the different style of English even after years (derived from Latin and Indo European class of languages)
Sales jobs tend to go to American trained Chinese or to Westerners.
In Indian software companies, accent is a lesser problem.
The biggest threat to Indian software in 5 years is actually Indian software itself- Can it evolve and mature to a product based model from a service only model.
Can Indian software partner with Chinese companies and maybe teach the Indian government why friendship is more profitable than envy and suspicion. If the US and China can trade enormously despite annual tensions, why cant Indian services do the same- if they lose this opportunity, US companies will likely bypass them and create the same GE/McKinsey style backoffices that started the Indian offshoring phenomenon.
3) Lastly- what did the poor American grad student do to deserve that even if devotes years to study STEM (and being called a Geek and Nerd) his job will get outsourced to India or China (if not now- in his 30s or worse in his 40s). Talk to any middle aged IT chap in the US who is middle class- and India and China would figure in why he still worries about his overpriced mortgage.
Unless the US wants only Twitter and Facebook as dominant technologies in the 21 st century.
- Carl Pope: India vs. China: Which Low-Carbon Development Model Will Win? (huffingtonpost.com)
- Indian miracle will help outpace Chinese economy: Economist (topinews.com)
- Winning Chinese hearts through yoga (thehindu.com)
- You: India grappling with the China syndrome (search.japantimes.co.jp)
- India will soon start to outpace China: Economist (topinews.com)
- Leo Hindery, Jr.: China’s latest powerplays – more unfair trade, now grave threats to our security (huffingtonpost.com)
- Manufacturers Reluctant to Reveal Codes to Indian Government (nytimes.com)
- Can India Beat China? (trak.in)
- In China, many younger military leaders view America as the ultimate enemy (zdnet.com)