JMP and R – #rstats

An amazing example of R being used sucessfully in combination (and not is isolation) with other enterprise software is the add-ins functionality of JMP and it’s R integration.

See the following JMP add-ins which use R

JMP Add-in: Multidimensional Scaling using R

This add-in creates a new menu command under the Add-Ins Menu in the submenu R Add-ins. The script will launch a custom dialog (or prompt for a JMP data table is one is not already open) where you can cast columns into roles for performing MDS on the data table. The analysis results in a data table of MDS dimensions and associated output graphics. MDS is a dimension reduction method that produces coordinates in Euclidean space (usually 2D, 3D) that best represent the structure of a full distance/dissimilarity matrix. MDS requires that input be a symmetric dissimilarity matrix. Input to this application can be data that is already in the form of a symmetric dissimilarity matrix or the dissimilarity matrix can be computed based on the input data (where dissimilarity measures are calculated between rows of the input data table in R).

Submitted by: Kelci Miclaus SAS employee Initiative: All
Application: Add-Ins Analysis: Exploratory Data Analysis

Chernoff Faces Add-in

One way to plot multivariate data is to use Chernoff faces. For each observation in your data table, a face is drawn such that each variable in your data set is represented by a feature in the face. This add-in uses JMP’s R integration functionality to create Chernoff faces. An R install and the TeachingDemos R package are required to use this add-in.

Submitted by: Clay Barker SAS employee Initiative: All
Application: Add-Ins Analysis: Data Visualization

Support Vector Machine for Classification

By simply opening a data table, specifying X, Y variables, selecting a kernel function, and specifying its parameters on the user-friendly dialog, you can build a classification model using Support Vector Machine. Please note that R package ‘e1071’ should be installed before running this dialog. The package can be found from

Submitted by: Jong-Seok Lee SAS employee Initiative: All
Application: Add-Ins Analysis: Exploratory Data Analysis/Mining

Penalized Regression Add-in

This add-in uses JMP’s R integration functionality to provide access to several penalized regression methods. Methods included are the LASSO (least absolutee shrinkage and selection operator, LARS (least angle regression), Forward Stagewise, and the Elastic Net. An R install and the “lars” and “elasticnet” R packages are required to use this add-in.

Submitted by: Clay Barker SAS employee Initiative: All
Application: Add-Ins Analysis: Regression

MP Addin: Univariate Nonparametric Bootstrapping

This script performs simple univariate, nonparametric bootstrap sampling by using the JMP to R Project integration. A JMP Dialog is built by the script where the variable you wish to perform bootstrapping over can be specified. A statistic to compute for each bootstrap sample is chosen and the data are sent to R using new JSL functionality available in JMP 9. The boot package in R is used to call the boot() function and the function to calculate the sample statistic for each bootstrap sample and the basic bootstrap confidence interval. The results are brought back to JMP and displayed using the JMP Distribution platform.

Submitted by: Kelci Miclaus SAS employee Initiative: All
Application: Add-Ins Analysis: Basic Statistics

Revolution Webinar Series #Rstats

Revolution Analytics Webinar-


Featured Webinar
David Champagne REGISTER NOW
Presenter David Champagne
CTO, Revolution Analytics
Date Tuesday, December 20th
Time 11:00AM – 11:30AM Pacific 
Click here for the webinar time in your local time zone

Big Data Starts with R

Traditional IT infrastructure is simply unable to meet

the demands of the new “Big Data Analytics” landscape.   Many enterprises are turning to the “R” statistical programming language and Hadoop (both open source projects) as a potential solution. This webinar will introduce the statistical capabilities of R within the Hadoop ecosystem.  We’ll cover:

  • An introduction to new packages developed by Revolution Analytics to facilitate interaction with the data stores HDFS and HBase so that they can be leveraged from the R environment
  • An overview of how to write Map Reduce jobs in R using Hadoop
  • Special considerations that need to be made when working with R and Hadoop.

We’ll also provide additional resources that are available to people interested in integrating R and Hadoop.


Upcoming Webinars
Wed, Dec 14th
11:00AM – 11:30AM PT
Revolution R Enterprise – 100% R and MoreR users already know why the R language is the lingua franca of statisticians today: because it’s the most powerful statistical language in the world. Revolution Analytics builds on the power of open source R, and adds performance, productivity and integration features to create Revolution R Enterprise. In this webinar, author and blogger David Smith will introduce the additional capabilities of Revolution R Enterprise.
 Archived Webinars-
Revolution Webinar: New Features in Revolution R Enterprise 5.0 (including RevoScaleR) to Support Scalable Data AnalysisRevolution R Enterprise 5.0 is Revolution Analytics’ scalable analytics platform.  At its core is Revolution Analytics’ enhanced Distribution of R, the world’s most widely-used project for statistical computing.  In this webinar, Dr. Ranney will discuss new features and show examples of the new functionality, which extend the platform’s usability, integration and scalability


Graphs in Statistical Analysis

One of the seminal papers establishing the importance of data visualization (as it is now called) was the 1973 paper by F J Anscombe in

It has probably the most elegant introduction to an advanced statistical analysis paper that I have ever seen-

1. Usefulness of graphs

Most textbooks on statistical methods, and most statistical computer programs, pay too little attention to graphs. Few of us escape being indoctrinated with these notions:

(1) numerical calculations are exact, but graphs are rough;

(2) for any particular kind of statistical data there is just one set of calculations constituting a correct statistical analysis;

(3) performing intricate calculations is virtuous, whereas actually looking at the data is cheating.

A computer should make both calculations and graphs. Both sorts of output should be studied; each will contribute to understanding.

Of course the dataset makes it very very interesting for people who dont like graphical analysis too much.


 The x values are the same for the first three datasets.

Anscombe’s Quartet
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89

For all four datasets:

Property Value
Mean of x in each case 9 exact
Variance of x in each case 11 exact
Mean of y in each case 7.50 (to 2 decimal places)
Variance of y in each case 4.122 or 4.127 (to 3 d.p.)
Correlation between x and y in each case 0.816 (to 3 d.p.)
Linear regression line in each case y = 3.00 + 0.500x (to 2 d.p. and 3 d.p. resp.)
But see the graphical analysis –
While R has always been great in emphasizing graphical analysis, thanks in part due to work by H Wickham and others, SAS products and  language has also modified its approach at
 SAS Visual Data Discovery combines top-selling SAS products (Base SASSAS/STAT® and SAS/GRAPH®), along with two interfaces (SAS® Enterprise Guide® for guided tasks and batch analysis and JMP® software for discovery and exploratory analysis).
 and  ODS Statistical Graphs at
While ODS Statistical graphs is still not as smooth as say R’s GGPLOT2, it still is a progressive step
Pretty graphs make for better decisions too !



My Digital Trail

Someone I know recently mentioned that I have an extensive Digital Trail. I do.

I have 7863 connections at, 31 likes at and 19 likes at, 409 friends (and 13 subscribers) at .On twitter I have 499 followers at and 344 followers at , and even on Google Plus some 617 people circling me at (besides 6 other pages on G+)

Even my Youtube channel at is more popular than I am in non-digital life. my non existant video blog at and my poetry blog at, and my comments on other social media, and my blurbs on my tumblr, and you get a lot of my psych profile.

Why do I do leave so much trail digitally?

For one reason- I was a bit of introvert always and technology set me free, the opportunity to think and yet be relaxed in anonymous chatter.

For the second reason- I am divorced and my wife got my 4 yr old son’s custody. Even though I talk to him once a day for a couple of minutes, somehow I hope when he grows, he reads my digital trail , maybe even these words, on the kind of man I was and the phases and seasons of life I went through.


That is all.



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Software as a Religion ( SaaR)

The decline of organized religion and debate about such matters in the Western Hemisphere has been co-related to the increase in debates and arguments (again mostly) in the Western Hemisphere on software. Be it the PC vs Mac, the Microsofties vs Open Sourcers, the not so evil Google versus fans of Facebook, considerable activity is now being done by human beings in terms of social interaction on the merit’s and demerit’s of each software bundle. Perhaps for the first time in human history these interactions are being captured digitally on medium (that is hopefully longer lasting than papyrus).

Will this lead to newer branches of psychologists, sociologists (Goodwin’s law is too simplistic but an effort)

Even software as a religion is plausible, all they need is another college drop-put whizkid  to find a way to make it effective.

Religion as a software has of course been around for several millennium.

Also see