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Predictive Models Ain’t Easy to Deploy

 

This is a guest blog post by Carole Ann Matignon of Sparkling Logic. You can see more on Sparkling Logic at http://my.sparklinglogic.com/

Decision Management is about combining predictive models and business rules to automate decisions for your business. Insurance underwriting, loan origination or workout, claims processing are all very good use cases for that discipline… But there is a hiccup… It ain’t as easy you would expect…

What’s easy?

If you have a neat model, then most tools would allow you to export it as a PMML model – PMML stands for Predictive Model Markup Language and is a standard XML representation for predictive model formulas. Many model development tools let you export it without much effort. Many BRMS – Business rules Management Systems – let you import it. Tada… The model is ready for deployment.

What’s hard?

The problem that we keep seeing over and over in the industry is the issue around variables.

Those neat predictive models are formulas based on variables that may or may not exist as is in your object model. When the variable is itself a formula based on the object model, like the min, max or sum of Dollar amount spent in Groceries in the past 3 months, and the object model comes with transaction details, such that you can compute it by iterating through those transactions, then the problem is not “that” big. PMML 4 introduced some support for those variables.

The issue that is not easy to fix, and yet quite frequent, is when the model development data model does not resemble the operational one. Your Data Warehouse very likely flattened the object model, and pre-computed some aggregations that make the mapping very hard to restore.

It is clearly not an impossible project as many organizations do that today. It comes with a significant overhead though that forces modelers to involve IT resources to extract the right data for the model to be operationalized. It is a heavy process that is well justified for heavy-duty models that were developed over a period of time, with a significant ROI.

This is a show-stopper though for other initiatives which do not have the same ROI, or would require too frequent model refresh to be viable. Here, I refer to “real” model refresh that involves a model reengineering, not just a re-weighting of the same variables.

For those initiatives where time is of the essence, the challenge will be to bring closer those two worlds, the modelers and the business rules experts, in order to streamline the development AND deployment of analytics beyond the model formula. The great opportunity I see is the potential for a better and coordinated tuning of the cut-off rules in the context of the model refinement. In other words: the opportunity to refine the strategy as a whole. Very ambitious? I don’t think so.

About Carole Ann Matignon

http://my.sparklinglogic.com/index.php/company/management-team

Carole-Ann Matignon Print E-mail

Carole-Ann MatignonCarole-Ann Matignon – Co-Founder, President & Chief Executive Officer

She is a renowned guru in the Decision Management space. She created the vision for Decision Management that is widely adopted now in the industry.  Her claim to fame is managing the strategy and direction of Blaze Advisor, the leading BRMS product, while she also managed all the Decision Management tools at FICO (business rules, predictive analytics and optimization). She has a vision for Decision Management both as a technology and a discipline that can revolutionize the way corporations do business, and will never get tired of painting that vision for her audience.  She speaks often at Industry conferences and has conducted university classes in France and Washington DC.

She started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication. At Cleversys (acquired by Kurt Salmon & Associates), she also conducted strategic consulting gigs around change management.

While playing with advanced software components, she found a passion for technology and joined ILOG (acquired by IBM). She developed a growing interest in Optimization as well as Business Rules. At ILOG, she coined the term BRMS while brainstorming with her Sales counterpart. She led the Presales organization for Telecom in the Americas up until 2000 when she joined Blaze Software (acquired by Brokat Technologies, HNC Software and finally FICO).

Her 360-degree experience allowed her to gain appreciation for all aspects of a software company, giving her a unique perspective on the business. Her technical background kept her very much in touch with technology as she advanced.

PMML Augustus

Here is a new-old system in open source for

for building and scoring statistical models designed to work with data sets that are too large to fit into memory.

http://code.google.com/p/augustus/

Augustus is an open source software toolkit for building and scoring statistical models. It is written in Python and its
most distinctive features are:
• Ability to be used on sets of big data; these are data sets that exceed either memory capacity or disk capacity, so
that existing solutions like R or SAS cannot be used. Augustus is also perfectly capable of handling problems
that can fit on one computer.
• PMML compliance and the ability to both:
– produce models with PMML-compliant formats (saved with extension .pmml).
– consume models from files with the PMML format.
Augustus has been tested and deployed on serveral operating systems. It is intended for developers who work in the
financial or insurance industry, information technology, or in the science and research communities.
Usage
Augustus produces and consumes Baseline, Cluster, Tree, and Ruleset models. Currently, it uses an event-based
approach to building Tree, Cluster and Ruleset models that is non-standard.

New to PMML ?

Read on http://code.google.com/p/augustus/wiki/PMML

The Predictive Model Markup Language or PMML is a vendor driven XML markup language for specifying statistical and data mining models. In other words, it is an XML language so that (more…)

Interview Eberhard Miethke and Dr. Mamdouh Refaat, Angoss Software

Here is an interview with Eberhard Miethke and Dr. Mamdouh Refaat, of Angoss Software. Angoss is a global leader in delivering business intelligence software and predictive analytics solutions that help businesses capitalize on their data by uncovering new opportunities to increase sales and profitability and to reduce risk. (more…)

Augustus- a PMML model producer and consumer. Scoring engine.

A Bold GNU Head

Image via Wikipedia

I just checked out this new software for making PMML models. It is called Augustus and is created by the Open Data Group (http://opendatagroup.com/) , which is headed by Robert Grossman, who was the first proponent of using R on Amazon Ec2.

Probably someone like Zementis ( http://adapasupport.zementis.com/ ) can use this to further test , enhance or benchmark on the Ec2. They did have a joint webinar with Revolution Analytics recently.

https://code.google.com/p/augustus/

Recent News

  • Augustus v 0.4.3.1 has been released
  • Added a guide (pdf) for including Augustus in the Windows System Properties.
  • Updated the install documentation.
  • Augustus 2010.II (Summer) release is available. This is v 0.4.2.0. More information is here.
  • Added performance discussion concerning the optional cyclic garbage collection.

See Recent News for more details and all recent news.

Augustus

Augustus is a PMML 4-compliant scoring engine that works with segmented models. Augustus is designed for use with statistical and data mining models. The new release provides Baseline, Tree and Naive-Bayes producers and consumers.

There is also a version for use with PMML 3 models. It is able to produce and consume models with 10,000s of segments and conforms to a PMML draft RFC for segmented models and ensembles of models. It supports Baseline, Regression, Tree and Naive-Bayes.

Augustus is written in Python and is freely available under the GNU General Public License, version 2.

See the page Which version is right for me for more details regarding the different versions.

PMML

Predictive Model Markup Language (PMML) is an XML mark up language to describe statistical and data mining models. PMML describes the inputs to data mining models, the transformations used to prepare data for data mining, and the parameters which define the models themselves. It is used for a wide variety of applications, including applications in finance, e-business, direct marketing, manufacturing, and defense. PMML is often used so that systems which create statistical and data mining models (“PMML Producers”) can easily inter-operate with systems which deploy PMML models for scoring or other operational purposes (“PMML Consumers”).

Change Detection using Augustus

For information regarding using Augustus with Change Detection and Health and Status Monitoring, please see change-detection.

Open Data

Open Data Group provides management consulting services, outsourced analytical services, analytic staffing, and expert witnesses broadly related to data and analytics. It has experience with customer data, supplier data, financial and trading data, and data from internal business processes.

It has staff in Chicago and San Francisco and clients throughout the U.S. Open Data Group began operations in 2002.


Overview

The above example contains plots generated in R of scoring results from Augustus. Each point on the graph represents a use of the scoring engine and a chart is an aggregation of multiple Augustus runs. A Baseline (Change Detection) model was used to score data with multiple segments.

Typical Use

Augustus is typically used to construct models and score data with models. Augustus includes a dedicated application for creating, or producing, predictive models rendered as PMML-compliant files. Scoring is accomplished by consuming PMML-compliant files describing an appropriate model. Augustus provides a dedicated application for scoring data with four classes of models, Baseline (Change Detection) ModelsTree ModelsRegression Models and Naive Bayes Models. The typical model development and use cycle with Augustus is as follows:

  1. Identify suitable data with which to construct a new model.
  2. Provide a model schema which proscribes the requirements for the model.
  3. Run the Augustus producer to obtain a new model.
  4. Run the Augustus consumer on new data to effect scoring.

Separate consumer and producer applications are supplied for Baseline (Change Detection) models, Tree models, Regression models and for Naive Bayes models. The producer and consumer applications require configuration with XML-formatted files. The specification of the configuration files and model schema are detailed below. The consumers provide for some configurability of the output but users will often provide additional post-processing to render the output according to their needs. A variety of mechanisms exist for transmitting data but user’s may need to provide their own preprocessing to accommodate their particular data source.

In addition to the producer and consumer applications, Augustus is conceptually structured and provided with libraries which are relevant to the development and use of Predictive Models. Broadly speaking, these consist of components that address the use of PMML and components that are specific to Augustus.

Post Processing

Augustus can accommodate a post-processing step. While not necessary, it is often useful to

  • Re-normalize the scoring results or performing an additional transformation.
  • Supplements the results with global meta-data such as timestamps.
  • Formatting of the results.
  • Select certain interesting values from the results.
  • Restructure the data for use with other applications.

PMML Plugin for Greenplum now available

Predictive Model Markup Language

Image via Wikipedia

From a press release from Zementis.

 

, the Universal PMML Plug-in for in-database scoring. Available now for the EMC Greenplum Database, a high-performance massively parallel processing (MPP) database, the plug-in leverages the Predictive Model Markup Language (PMML) to execute predictive models directly within EMC Greenplum, for highly optimized in-database scoring.

Universal PMML Plug-in

Developed by the Data Mining Group (DMG), PMML is supported by all major data mining vendors, e.g., IBM SPSS, SAS, Teradata, FICO, STASTICA, Microstrategy, TIBCO and Revolution Analytics as well as open source tools like R, KNIME and RapidMiner. With PMML, models built in any of these data mining tools can now instantly be deployed in the EMC Greenplum database. The net result is the ability to leverage the power of standards-based predictive analytics on a massive scale, right where the data resides.

“By partnering with Zementis, a true PMML innovator, we are able to offer a vendor-agnostic solution for moving enterprise-level predictive analytics into the database execution environment,” said Dr. Steven Hillion, Vice President of Analytics at EMC Greenplum. “With Zementis and PMML, the de-facto standard for representing data mining models, we are eliminating the need to recode predictive analytic models in order to deploy them within our database. In turn, this enables an analyst to reduce the time to insight required in most businesses today.”

Want to learn more?
 

To learn more about how the EMC Greenplum Database and the Universal PMML Plug-in work together, feel free to:

  1. Visit the PMML Plug-in product page
  2. Download the white paper

The Universal PMML Plug-in for the EMC Greenplum Database is available now. Contact us today for more information.

Michael Zeller, CEO, Zementis

 

 

Scoring SAS and SPSS Models in the cloud

Outline of a cloud containing text 'The Cloud'

Image via Wikipedia

An announcement from Zementis and Predixion Software- about using cloud computing for scoring models using PMML. Note R has a PMML package as well which is used by Rattle, data mining GUI for exporting models.

Source- http://www.marketwatch.com/story/predixion-software-introduces-new-product-to-run-sas-and-spss-predictive-models-in-the-cloud-2010-10-19?reflink=MW_news_stmp

——————————————————————————————————–

ALISO VIEJO, Calif., Oct 19, 2010 (BUSINESS WIRE) — Predixion Software today introduced Predixion PMML Connexion(TM), an interface that provides Predixion Insight(TM), the company’s low-cost, self-service in the cloud predictive analytics solution, direct and seamless access to SAS, SPSS (IBM) and other predictive models for use by Predixion Insight customers. Predixion PMML Connexion enables companies to leverage their significant investments in legacy predictive analytics solutions at a fraction of the cost of conventional licensing and maintenance fees.

The announcement was made at the Predictive Analytics World conference in Washington, D.C. where Predixion also announced a strategic partnership with Zementis, Inc., a market leader in PMML-based solutions. Zementis is exhibiting in Booth #P2.

The Predictive Model Markup Language (PMML) standard allows for true interoperability, offering a mature standard for moving predictive models seamlessly between platforms. Predixion has fully integrated this PMML functionality into Predixion Insight, meaning Predixion Insight users can now effortlessly import PMML-based predictive models, enabling information workers to score the models in the cloud from anywhere and publish reports using Microsoft Excel(R) and SharePoint(R). In addition, models can also be written back into SAS, SPSS and other platforms for a truly collaborative, interoperable solution.

“Predixion’s investment in this PMML interface makes perfect business sense as the lion’s share of the models in existence today are created by the SAS and SPSS platforms, creating compelling opportunity to leverage existing investments in predictive and statistical models on a low-cost cloud predictive analytics platform that can be fed with enterprise, line of business and cloud-based data,” said Mike Ferguson, CEO of Intelligent Business Strategies, a leading analyst and consulting firm specializing in the areas of business intelligence and enterprise business integration. “In this economy, Predixion’s low-cost, self-service predictive analytics solutions might be welcome relief to IT organizations chartered with quickly adding additional applications while at the same time cutting costs and staffing.”

“We are pleased to be partnering with Zementis, truly a PMML market leader and innovator,” said Predixion CEO Simon Arkell. “To allow any SAS or SPSS customer to immediately score any of their predictive models in the cloud from within Predixion Insight, compare those models to those created by Predixion Insight, and share the results within Excel and Sharepoint is an exciting step forward for the industry. SAS and SPSS customers are fed up with the high prices they must pay for their business users just to access reports generated by highly skilled PhDs who are burdened by performing routine tasks and thus have become a massive bottleneck. That frustration is now a thing of the past because any information worker can now unlock the power of predictive analytics without relying on experts — for a fraction of the cost and from anywhere they can connect to the cloud,” Arkell said.

Dr. Michael Zeller, Zementis CEO, added, “Our mission is to significantly shorten the time-to-market for predictive models in any industry. We are excited to be contributing to Predixion’s self-service, cloud-based predictive analytics solution set.”

About Predixion Software

Predixion Software develops and markets collaborative predictive analytics solutions in the public and private cloud. Predixion enables self-service predictive analytics, allowing customers to use and analyze large amounts of data to make actionable decisions, all within the familiar environment of Excel and PowerPivot. Predixion customers are achieving immediate results across a multitude of industries including: retail, finance, healthcare, marketing, telecommunications and insurance/risk management.

Predixion Software is headquartered in Aliso Viejo, California with development offices in Redmond, Washington. The company has venture capital backing from established investors including DFJ Frontier, Miramar Venture Partners and Palomar Ventures. For more information please contact us at 949-330-6540, or visit us atwww.predixionsoftware.com.

About Zementis

Zementis, Inc. is a leading software company focused on the operational deployment and integration of predictive analytics and data mining solutions. Its ADAPA(R) decision engine successfully bridges the gap between science and engineering. ADAPA(R) was designed from the ground up to benefit from open standards and to significantly shorten the time-to-market for predictive models in any industry. For more information, please visit www.zementis.com.

 

Interfaces to R

This is a fairly long post and is a basic collection  of material for a book/paper. It is on interfaces to use R. If you feel I need to add more on a  particular R interface, or if there is an error in this- please feel to contact me on twitter @decisionstats or mail ohri2007 on google mail.

R Interfaces

There are multiple ways to use the R statistical language.

Command Line- The default method is using the command prompt by the installed software on download from http://r-project.org
For windows users there is a simple GUI which has an option for Packages (loading package, installing package, setting CRAN mirror for downloading packages) , Misc (useful for listing all objects loaded in workspace as well as clearing objects to free up memory), and Help Menu.

Using Click and Point- Besides the command prompt, there are many Graphical User Interfaces which enable the analyst to use click and point methods to analyze data without getting into the details of learning complex and at times overwhelming R syntax. R GUIs are very popular both as mode of instruction in academia as well as in actual usage as it cuts down considerably on time taken to adapt to the language. As with all command line and GUI software, for advanced tweaks and techniques, command prompt will come in handy as well.

Advantages and Limitations of using Visual Programming Interfaces to R as compared to Command Line.

 

Advantages Limitations
Faster learning for new programmers Can create junk analysis by clicking menus in GUI
Easier creation of advanced models or graphics Cannot create custom functions unless you use command line
Repeatability of analysis is better Advanced techniques and custom flexibility of data handling R can be done in command line
Syntax is auto-generated Can limit scope and exposure in learning R syntax




A brief list of the notable Graphical User Interfaces is below-

1) R Commander- Basic statistics
2) Rattle- Data Mining
3) Deducer- Graphics (including GGPlot Integration) and also uses JGR (a Jave based  GUI)
4) RKward- Comprehensive R GUI for customizable graphs
5) Red-R – Dataflow programming interface using widgets

1) R Commander- R Commander was primarily created by Professor John Fox of McMaster University to cover the content of a basic statistics course. However it is extensible and many other packages can be added in menu form to it- in the form R Commander Plugins. Quite noticeably it is one of the most widely used R GUI and it also has a script window so you can write R code in combination with the menus.
As you point and click a particular menu item, the corresponding R code is automatically generated in the log window and executed.

It can be found on CRAN at http://cran.r-project.org/web/packages/Rcmdr/index.html



Advantages of Using  R Commander-
1) Useful for beginner in R language to do basic graphs and analysis and building models.
2) Has script window, output window and log window (called messages) in same screen which helps user as code is auto-generated on clicking on menus, and can be customized easily. For example in changing labels and options in Graphs.  Graphical output is shown in seperate window from output window.
3) Extensible for other R packages like qcc (for quality control), Teaching Demos (for training), survival analysis and Design of Experiments (DoE)
4) Easy to understand interface even for first time user.
5) Menu items which are not relevant are automatically greyed out- if there are only two variables, and you try to build a 3D scatterplot graph, that menu would simply not be available and is greyed out.

Comparative Disadvantages of using R Commander-
1) It is basically aimed at a statistical audience( originally students in statistics) and thus the terms as well as menus are accordingly labeled. Hence it is more of a statistical GUI rather than an analytics GUI.
2) Has limited ability to evaluate models from a business analysts perspective (ROC curve is not given as an option) even though it has extensive statistical tests for model evaluation in model sub menu. Indeed creating a Model is treated as a subsection of statistics rather than a separate menu item.
3) It is not suited for projects that do not involve advanced statistical testing and for users not proficient in statistics (particularly hypothesis testing), and for data miners.

Menu items in the R Commander window:
File Menu – For loading script files and saving Script files, Output and Workspace
It is also needed for changing the present working directory and for exiting R.
Edit Menu – For editing scripts and code in the script window.
Data Menu - For creating new dataset, inputting or importing data and manipulating data through variables. Data Import can be from text,comma separated values,clipboard, datasets from SPSS, Stata,Minitab, Excel ,dbase,  Access files or from url.
Data manipulation included deleting rows of data as well as manipulating variables.
Also this menu has the option for merging two datasets by row or columns.
Statistics Menu-This menu has options for descriptive statistics, hypothesis tests, factor analysis and clustering and also for creating models. Note there is a separate menu for evaluating the model so created.
Graphs Menu-It has options for creating various kinds of graphs including box-plot, histogram, line, pie charts and x-y plots.
The first option is color palette- it can be used for customizing the colors. It is recommended you adjust colors based on your need for publication or presentation.
A notable option is 3 D graphs for evaluating 3 variables at a time- this is really good and impressive feature and exposes the user to advanced graphs in R all at few clicks. You may want to dazzle a presentation using this graph.
Also consider scatterplot matrix graphs for graphical display of variables.
Graphical display of R surpasses any other statistical software in appeal as well as ease of creation- using GUI to create graphs can further help the user to get the most of data insights using R at a very minimum effort.
Models Menu-This is somewhat of a labeling peculiarity of R Commander as this menu is only for evaluating models which have been created using the statistics menu-model sub menu.
It includes options for graphical interpretation of model results,residuals,leverage and confidence intervals and adding back residuals to the data set.
Distributions Menu- is for cumulative probabilities, probability density, graphs of distributions, quantiles and features for standard distributions and can be used in lieu of standard statistical tables for the distributions. It has 13 standard statistical continuous distributions and 5 discrete distributions.
Tools Menu- allows you to load other packages and also load R Commander plugins (which are then added to the Interface Menu after the R Commander GUI is restarted). It also contains options sub menu for fine tuning (like opting to send output to R Menu)
Help Menu- Standard documentation and help menu. Essential reading is the short 25 page manual in it called Getting “Started With the R Commander”.

R Commander Plugins- There are twenty extensions to R Commander that greatly enhance it’s appeal -these include basic time series forecasting, survival analysis, qcc and more.

see a complete list at

  1. DoE – http://cran.r-project.org/web/packages/RcmdrPlugin.DoE/RcmdrPlugin.DoE.pdf
  2. doex
  3. EHESampling
  4. epack- http://cran.r-project.org/web/packages/RcmdrPlugin.epack/RcmdrPlugin.epack.pdf
  5. Export- http://cran.r-project.org/web/packages/RcmdrPlugin.Export/RcmdrPlugin.Export.pdf
  6. FactoMineR
  7. HH
  8. IPSUR
  9. MAc- http://cran.r-project.org/web/packages/RcmdrPlugin.MAc/RcmdrPlugin.MAc.pdf
  10. MAd
  11. orloca
  12. PT
  13. qcc- http://cran.r-project.org/web/packages/RcmdrPlugin.qcc/RcmdrPlugin.qcc.pdf and http://cran.r-project.org/web/packages/qcc/qcc.pdf
  14. qual
  15. SensoMineR
  16. SLC
  17. sos
  18. survival-http://cran.r-project.org/web/packages/RcmdrPlugin.survival/RcmdrPlugin.survival.pdf
  19. SurvivalT
  20. 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.
You can read more on R Commander Plugins at http://wp.me/p9q8Y-1Is
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Rattle- R Analytical Tool To Learn Easily (download from http://rattle.togaware.com/)
Rattle is more advanced user Interface than R Commander though not as popular in academia. It has been designed explicitly for data mining and it also has a commercial version for sale by Togaware. Rattle has a Tab and radio button/check box rather than Menu- drop down approach towards the graphical design. Also the Execute button needs to be clicked after checking certain options, just the same as submit button is clicked after writing code. This is different from clicking on a drop down menu.

Advantages of Using Rattle
1) Useful for beginner in R language to do building models,cluster and data mining.
2) Has separate tabs for data entry,summary, visualization,model building,clustering, association and evaluation. The design is intuitive and easy to understand even for non statistical background as the help is conveniently explained as each tab, button is clicked. Also the tabs are placed in a very sequential and logical order.
3) Uses a lot of other R packages to build a complete analytical platform. Very good for correlation graph,clustering as well decision trees.
4) Easy to understand interface even for first time user.
5) Log  for R code is auto generated and time stamp is placed.
6) Complete solution for model building from partitioning datasets randomly for testing,validation to building model, evaluating lift and ROC curve, and exporting PMML output of model for scoring.
7) Has a well documented online help as well as in-software documentation. The help helps explain terms even to non statistical users and is highly useful for business users.

Example Documentation for Hypothesis Testing in Test Tab in Rattle is ”
Distribution of the Data
* Kolomogorov-Smirnov     Non-parametric Are the distributions the same?
* Wilcoxon Signed Rank    Non-parametric Do paired samples have the same distribution?
Location of the Average
* T-test               Parametric     Are the means the same?
* Wilcoxon Rank-Sum    Non-parametric Are the medians the same?
Variation in the Data
* F-test Parametric Are the variances the same?
Correlation
* Correlation    Pearsons Are the values from the paired samples correlated?”

Comparative Disadvantages of using Rattle-
1) It is basically aimed at a data miner.  Hence it is more of a data mining GUI rather than an analytics GUI.
2) Has limited ability to create different types of graphs from a business analysts perspective Numeric variables can be made into Box-Plot, Histogram, Cumulative as well Benford Graphs. While interactivity using GGobi and Lattiticist is involved- the number of graphical options is still lesser than other GUI.
3) It is not suited for projects that involve multiple graphical analysis and which do not have model building or data mining.For example Data Plot is given in clustering tab but not in general Explore tab.
4) Despite the fact that it is meant for data miners, no support to biglm packages, as well as parallel programming is enabled in GUI for bigger datasets, though these can be done by R command line in conjunction with the Rattle GUI. Data m7ining is typically done on bigger datsets.
5) May have some problems installing it as it is dependent on GTK and has a lot of packages as dependencies.

Top Row-
This has the Execute Button (shown as two gears) and which has keyboard shortcut F2. It is used to execute the options in Tabs-and is equivalent of submit code button.
Other buttons include new Projects,Save  and Load projects which are files with extension to .rattle an which store all related information from Rattle.
It also has a button for exporting information in the current Tab as an open office document, and buttons for interrupting current process as well as exiting Rattle.

Data Tab-
It has the following options.
●        Data Type- These are radio buttons between Spreadsheet (and Comma Separated Values), ARFF files (Weka), ODBC (for Database Connections),Library (for Datasets from Packages),R Dataset or R datafile, Corpus (for Text Mining) and Script for generating the data by code.
●        The second row-in Data Tab in Rattle is Detail on Data Type- and its apperance shifts as per the radio button selection of data type in previous step. For Spreadsheet, it will show Path of File, Delimiters, Header Row while for ODBC it will show DSN, Tables, Rows and for Library it will show you a dropdown of all datasets in all R packages installed locally.
●        The third row is a Partition field for splitting dataset in training,testing,validation and it shows ratio. It also specifies a Random seed which can be customized for random partitions which can be replicated. This is very useful as model building requires model to be built and tested on random sub sets of full dataset.
●        The fourth row is used to specify the variable type of inputted data. The variable types are
○        Input: Used for modeling as independent variables
○        Target: Output for modeling or the dependent variable. Target is a categoric variable for classification, numeric for regression and for survival analysis both Time and Status need to be defined
○        Risk: A variable used in the Risk Chart
○        Ident: An identifier for unique observations in the data set like AccountId or Customer Id
○        Ignore: Variables that are to be ignored.
●        In addition the weight calculator can be used to perform mathematical operations on certain variables and identify certain variables as more important than others.

Explore Tab-
Summary Sub-Tab has Summary for brief summary of variables, Describe for detailed summary and Kurtosis and Skewness for comparing them across numeric variables.
Distributions Sub-Tab allows plotting of histograms, box plots, and cumulative plots for numeric variables and for categorical variables Bar Plot and Dot Plot.
It also has Benford Plot for Benford’s Law on probability of distribution of digits.
Correlation Sub-Tab- This displays corelation between variables as a table and also as a very nice plot.
Principal Components Sub-Tab- This is for use with Principal Components Analysis including the SVD (singular value decomposition) and Eigen methods.
Interactive Sub-Tab- Allows interactive data exploration using GGobi and Lattice software. It is a powerful visual tool.

Test Tab-This has options for hypothesis testing of data for two sample tests.
Transform Tab-This has options for rescaling data, missing values treatment, and deleting invalid or missing values.
Cluster Tab-It gives an option to KMeans, Hierarchical and Bi-Cluster clustering methods with automated graphs,plots (including dendogram, discriminant plot and data plot) and cluster results available. It is highly recommended for clustering projects especially for people who are proficient in clustering but not in R.

Associate Tab-It helps in building association rules between categorical variables, which are in the form of “if then”statements. Example. If day is Thursday, and someone buys Milk, there is 80% chance they will buy Diapers. These probabilities are generated from observed frequencies.

Model Tab-The Model tab makes Rattle one of the most advanced data mining tools, as it incorporates decision trees(including boosted models and forest method), linear and logistic regression, SVM,neural net,survival models.
Evaluate Tab-It as functionality for evaluating models including lift,ROC,confusion matrix,cost curve,risk chart,precision, specificity, sensitivity as well as scoring datasets with built model or models. Example – A ROC curve generated by Rattle for Survived Passengers in Titanic (as function of age,class,sex) This shows comparison of various models built.

Log Tab- R Code is automatically generated by Rattle as the respective operation is executed. Also timestamp is done so it helps in reviewing error as well as evaluating speed for code optimization.
—————————————————————————————————————————-
JGR- Deducer- (see http://www.deducer.org/pmwiki/pmwiki.php?n=Main.DeducerManual
JGR is a Java Based GUI. Deducer is recommended for use with JGR.
Deducer has basically been made to implement GGPLOT in a GUI- an advanced graphics package based on Grammer of Graphics and was part of Google Summer of Code project.

It first asks you to either open existing dataset or load a new dataset with just two icons. It has two initial views in Data Viewer- a Data view and Variable view which is quite similar to Base SPSS. The other Deducer options are loaded within the JGR console.

Advantages of Using  Deducer
1.      It has an option for factor as well as reliability analysis which is missing in other graphical user interfaces like R Commander and Rattle.
2.      The plot builder option gives very good graphics -perhaps the best in other GUIs. This includes a color by option which allows you to shade the colors based on variable value. An addition innovation is the form of templates which enables even a user not familiar with data visualization to choose among various graphs and click and drag them to plot builder area.
3.      You can set the Java Gui for R (JGR) menu to automatically load some packages by default using an easy checkbox list.
4.      Even though Deducer is a very young package, it offers a way for building other R GUIs using Java Widgets.
5.      Overall feel is of SPSS (Base GUI) to it’s drop down menu, and selecting variables in the sub menu dialogue by clicking to transfer to other side.SPSS users should be more comfortable at using this.
6.      A surprising thing is it rearranges the help documentation of all R in a very presentable and organized manner
7.      Very convenient to move between two or more datasets using dropdown.
8.      The most convenient GUI for merging two datasets using common variable.

Dis Advantages of Using  Deducer
1.      Not able to save plots as images (only options are .pdf and .eps), you can however copy as image.
2.      Basically a data viualization GUI – it does offer support for regression, descriptive statistics in the menu item Extras- however the menu suggests it is a work in progress.
3.      Website for help is outdated, and help documentation specific to Deducer lacks detail.



Components of Deducer-
Data Menu-Gives options for data manipulation including recoding variables,transform variables (binning, mathematical operation), sort dataset,  transpose dataset ,merge two datasets.
Analysis Menu-Gives options for frequency tables, descriptive statistics,cross tabs, one sample tests (with plots) ,two sample tests (with plots),k sample tests, correlation,linear and logistic models,generalized linear models.
Plot Builder Menu- This allows plots of various kinds to be made in an interactive manner.

Correlation using Deducer.

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Red-R – A dataflow user interface for R (see http://red-r.org/

Red R uses dataflow concepts as a user interface rather than menus and tabs. Thus it is more similar to Enterprise Miner or Rapid Miner in design. For repeatable analysis dataflow programming is preferred by some analysts. Red-R is written in Python.


Advantages of using Red-R
1) Dataflow style makes it very convenient to use. It is the only dataflow GUI for R.
2) You can save the data as well as analysis in the same file.
3) User Interface makes it easy to read R code generated, and commit code.
4) For repeatable analysis-like reports or creating models it is very useful as you can replace just one widget and other widget/operations remain the same.
5) Very easy to zoom into data points by double clicking on graphs. Also to change colors and other options in graphs.
6) One minor feature- It asks you to set CRAN location just once and stores it even for next session.
7) Automated bug report submission.

Disadvantages of using Red-R
1) Current version is 1.8 and it needs a lot of improvement for building more modeling types as well as debugging errors.
2) Limited features presently.
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RKWard (see http://rkward.sourceforge.net/)

It is primarily a KDE GUI for R, so it can be used on Ubuntu Linux. The windows version is available but has some bugs.

Advantages of using RKWard
1) It is the only R GUI for time series at present.
In addition it seems like the only R GUI explicitly for Item Response Theory (which includes credit response models,logistic models) and plots contains Pareto Charts.
2) It offers a lot of detail in analysis especially in plots(13 types of plots), analysis and  distribution analysis ( 8 Tests of normality,14 continuous and 6 discrete distributions). This detail makes it more suitable for advanced statisticians rather than business analytics users.
3) Output can be easily copied to Office documents.

Disadvantages of using RKWard
1) It does not have stable Windows GUI. Since a graphical user interface is aimed at making interaction easier for users- this is major disadvantage.
2) It has a lot of dependencies so may have some issues in installing.
3) The design categorization of analysis,plots and distributions seems a bit unbalanced considering other tabs are File, Edit, View, Workspace,Run,Settings, Windows,Help.
Some of the other tabs can be collapsed, while the three main tabs of analysis,plots,distributions can be better categorized (especially into modeling and non-modeling analysis).
4) Not many options for data manipulation (like subset or transpose) by the GUI.
5) Lack of detail in documentation as it is still on version 0.5.3 only.

Components-
Analysis, Plots and Distributions are the main components and they are very very extensive, covering perhaps the biggest range of plots,analysis or distribution analysis that can be done.
Thus RKWard is best combined with some other GUI, when doing advanced statistical analysis.

 

GNU General Public License
Image via Wikipedia

GrapherR

GrapheR is a Graphical User Interface created for simple graphs.

Depends: R (>= 2.10.0), tcltk, mgcv
Description: GrapheR is a multiplatform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graphs are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.
License: GPL-2
LazyLoad: yes
Packaged: 2011-01-24 17:47:17 UTC; Maxime
Repository: CRAN
Date/Publication: 2011-01-24 18:41:47

More information about GrapheR at CRAN
Path: /cran/newpermanent link

Advantages of using GrapheR

  • It is bi-lingual (English and French) and can import in text and csv files
  • The intention is for even non users of R, to make the simple types of Graphs.
  • The user interface is quite cleanly designed. It is thus aimed as a data visualization GUI, but for a more basic level than Deducer.
  • Easy to rename axis ,graph titles as well use sliders for changing line thickness and color

Disadvantages of using GrapheR

  • Lack of documentation or help. Especially tips on mouseover of some options should be done.
  • Some of the terms like absicca or ordinate axis may not be easily understood by a business user.
  • Default values of color are quite plain (black font on white background).
  • Can flood terminal with lots of repetitive warnings (although use of warnings() function limits it to top 50)
  • Some of axis names can be auto suggested based on which variable s being chosen for that axis.
  • Package name GrapheR refers to a graphical calculator in Mac OS – this can hinder search engine results

Using GrapheR

  • Data Input -Data Input can be customized for CSV and Text files.
  • GrapheR gives information on loaded variables (numeric versus Factors)
  • It asks you to choose the type of Graph 
  • It then asks for usual Graph Inputs (see below). Note colors can be customized (partial window). Also number of graphs per Window can be easily customized 
  • Graph is ready for publication



Related Articles

 

Summary of R GUIs


Using R from other software- Please note that interfaces to R exist from other software as well. These include software from SAS Institute, IBM SPSS, Rapid Miner,Knime  and Oracle.

A brief list is shown below-

1) SAS/IML Interface to R- You can read about the SAS Institute’s SAS/ IML Studio interface to R at http://www.sas.com/technologies/analytics/statistics/iml/index.html
2) Rapid  Miner Extension to R-You can view integration with Rapid Miner’s extension to R here at http://www.youtube.com/watch?v=utKJzXc1Cow
3) IBM SPSS plugin for R-SPSS software has R integration in the form of a plugin. This was one of the earliest third party software offering interaction with R and you can read more at http://www.spss.com/software/statistics/developer/
4) Knime- Konstanz Information Miner also has R integration. You can view this on
http://www.knime.org/downloads/extensions
5) Oracle Data Miner- Oracle has a data mining offering to it’s very popular database software which is integrated with the R language. The R Interface to Oracle Data Mining ( R-ODM) allows R users to access the power of Oracle Data Mining’s in-database functions using the familiar R syntax. http://www.oracle.com/technetwork/database/options/odm/odm-r-integration-089013.html
6) JMP- JMP version 9 is the latest to offer interface to R.  You can read example scripts here at http://blogs.sas.com/jmp/index.php?/archives/298-JMP-Into-R!.html

R Excel- Using R from Microsoft Excel

Microsoft Excel is the most widely used spreadsheet program for data manipulation, entry and graphics. Yet as dataset sizes have increased, Excel’s statistical capabilities have lagged though it’s design has moved ahead in various product versions.

R Excel basically works at adding a .xla plugin to
Excel just like other Plugins. It does so by connecting to R through R packages.

Basically it offers the functionality of R
functions and capabilities to the most widely distributed spreadsheet program. All data summaries, reports and analysis end up in a spreadsheet-

R Excel enables R to be very useful for people not
knowing R. In addition it adds (by option) the menus of R Commander as menus in Excel spreadsheet.


Advantages-
Enables R and Excel to communicate thus tieing an advanced statistical tool to the most widely used business analytics tool.

Disadvantages-
No major disadvatage at all to a business user. For a data statistical user, Microsoft Excel is limited to 100,000 rows, so R data needs to be summarized or reduced.

Graphical capabilities of R are very useful, but to a new user, interactive graphics in Excel may be easier than say using Ggplot ot Ggobi.
You can read more on this at http://rcom.univie.ac.at/ or  the complete Springer Book http://www.springer.com/statistics/computanional+statistics/book/978-1-4419-0051-7

The combination of cloud computing and internet offers a new kind of interaction possible for scientists as well analysts.

Here is a way to use R on an Amazon EC2 machine, thus renting by hour hardware and computing resources which are scaleable to massive levels , whereas the software is free.

Here is how you can connect to Amazon EC2 and run R.
Running R for Cloud Computing.
1) Logging onto Amazon Console http://aws.amazon.com/ec2/
Note you need your Amazon Id (even the same id which you use for buying books).Note we are into Amazon EC2 as shown by the upper tab. Click upper tab to get into the Amazon EC2
2) Choosing the right AMI-On the left margin, you can click AMI -Images. Now you can search for the image-I chose Ubuntu images (linux images are cheaper) and latest Ubuntu Lucid  in the search .You can choose whether you want 32 bit or 64 bit image. 64 bit images will lead to  faster processing of data.Click on launch instance in the upper tab ( near the search feature). A pop up comes up, which shows the 5 step process to launch your computing.
3) Choose the right compute instance- – there are various compute instances and they all are at different multiples of prices or compute units. They differ in terms of RAM memory and number of processors.After choosing the compute instance of your choice (extra large is highlighted)- click on continue-
4) Instance Details-Do not  choose cloudburst monitoring if you are on a budget as it has a extra charge. For critical production it would be advisable to choose cloudburst monitoring once you have become comfortable with handling cloud computing..
5) Add Tag Details- If you are running a lot of instances you need to create your own tags to help you manage them. It is advisable if you are going to run many instances.
6) Create a key pair- A key pair is an added layer of encryption. Click on create new pair and name it (note the name will be handy in coming steps)
7) After clicking and downloading the key pair- you come into security groups. Security groups is just a set of instructions to help keep your data transfer secure. You want to enable access to your cloud instance to certain IP addresses (if you are going to connect from fixed IP address and to certain ports in your computer. It is necessary in security group to enable  SSH using Port 22.
Last step- Review Details and Click Launch
8) On the Left margin click on instances ( you were in Images.>AMI earlier)
It will take some 3-5 minutes to launch an instance. You can see status as pending till then.
9) Pending instance as shown by yellow light-
10) Once the instance is running -it is shown by a green light.
Click on the check box, and on upper tab go to instance actions. Click on connect-
You see a popup with instructions like these-
· Open the SSH client of your choice (e.g., PuTTY, terminal).
·  Locate your private key, nameofkeypair.pem
·  Use chmod to make sure your key file isn’t publicly viewable, ssh won’t work otherwise:
chmod 400 decisionstats.pem
·  Connect to your instance using instance’s public DNS [ec2-75-101-182-203.compute-1.amazonaws.com].
Example
Enter the following command line:
ssh -i decisionstats2.pem root@ec2-75-101-182-203.compute-1.amazonaws.com

Note- If you are using Ubuntu Linux on your desktop/laptop you will need to change the above line to ubuntu@… from root@..

ssh -i yourkeypairname.pem -X ubuntu@ec2-75-101-182-203.compute-1.amazonaws.com

(Note X11 package should be installed for Linux users- Windows Users will use Remote Desktop)

12) Install R Commander on the remote machine (which is running Ubuntu Linux) using the command

sudo apt-get install r-cran-rcmdr


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