Welcome!

Open Source Cloud Authors: Yeshim Deniz, Roger Strukhoff, Jnan Dash, Ed Featherston, Elizabeth White

Blog Feed Post

Quick History: glm()

by Joseph Rickert I recently wrote about some R resources that are available for generalized linear models (GLMs). Looking over the material, I was amazed by the amount of effort that is continuing to go into GLMs, both with with respect to new theoretical developments and also in response to practical problems such as the need to deal with very large data sets. (See packages biglm, ff, ffbase, RevoScaleR for example.) This led me to wonder about the history of the GLM and its implementations. An adequate exploration of this topic would occupy a serious science historian (which I am definitely not) for a considerable amount of time. However, I think even a brief look at what apears to be the main line of the development of the GLM in R provides some insight into how good software influences statistical practice. A convenient place to start is with the 1972 paper Generalized Linear Models by Nelder and Wedderburn This seems to be the first paper  to give the GLM a life of its own.  The authors pulled things together by: grouping the Normal, Poisson, Binomial (probit) and gamma distributions together as members of the exponential family applying maximum likelihood estimation via the iteratively reweighted least squares algorithm to the family introducing the terminology “generalized linear models” suggesting  that this unification would be a pedagogic improvement that would “simplify the teaching of the subject to both specialists and non-specialists” It is clear that the GLM was not “invented” in 1972. But, Nelder and Wedderburn were able to package up statistical knowledge and a tradition of analysis going pretty far back in a way that will forever shape how statisticians think about generalizations of linear models. For a brief, but fairly detailed account of the history of the major developments in the in categorical data analysis, logistic regression and loglinear models in the early 20th century leading up to the GLM see Chapter 10 of Agresti 1996. (One very interesting fact highlighted by Agresti is that the iteratively reweighted least squares algorithm that Nelder and Weddergurn used to fit GLMs is the method that R.A. Fisher introduced in 1935 to for fitting probit models by means of maximum likelihood.) The first generally available software to implement a wide range of GLMs seems to have been the Fortran based GLIM system which was developed by the Royal Statistical Society’s Working Party on Statistical Computing, released in 1974 and developed through 1993. My guess is that GLIM dominated the field for nearly 20 years until it was eclipsed by the growing popularity of the 1991 version of S, and the introduction of PROC GENMOD in version 6.09 of SAS that was released in the 1993 timeframe. (Note that the first edition of the manual for the MatLab Statistics Toolbox also dates from 1993.) In any event, in the 1980s, the GLM became the “go to” statistical tool that it is today. In the chapter on Generalized Linear Models that they contributed to Chambers and Hastie’s landmark 1992 book, Hastie and Pregibon write that “GLMS have become popular over the past 10 years, partly due to the computer package GLIM …” It is dangerous temptation to attribute more to a quotation like this than the authors intended. Nevertheless, I think it does offer some support for the idea that in a field such as statistics, theory shapes the tools and then the shape of the tools exerts some influence on how the theory develops. R’s glm() function was, of course,  modeled on the S implementation, The stats package documentation states: The original R implementation of glm was written by Simon Davies working for Ross Ihaka at the University of Auckland, but has since been extensively re-written by members of the R Core team.The design was inspired by the S function of the same name described in Hastie & Pregibon (1992). I take this to mean that the R implementation of glm() was much more than just a direct port of the S code. glm() has come a long way. It is very likely that only the SAS PROC GENMOD implementation of the GLM has matched R’s glm()in popularity over the past decade. However, SAS’s closed environment has failed to match open-source R’s ability to foster growth and stimulate creativity. The performance, stability and rock solid reliability of glm() has contributed to making GLMs a basic tool both for statisticians and for the new generation of data scientists as well.   How GLM implementations will develop outside of R in the future is not clear at all. Python’s evolving glm implementation appears to be in the GLIM tradition. (The Python documentation references the paper by Green (1984) which, in-turn, references GLIM.) Going back to first principles is always a good idea, however Python's GLM function apparently only supports one parameter exponential families. The Python developers have a long way to go before they can match R's rich functionality.The Julia glm function is clearly being modeled after R and shows much promise. However, recent threads on the julia-stats google group forum indicate that the Julia developers are just now beginning to work on basic glm() functionality. ReferencesAgresti, Alan, An Introduction to Categorical Data Analysis: John Wiley and Sons (1996)Chambers, John M. and Trevor J. Hastie (ed.), Statistical Models In S: Wadsworth & Brooks /Cole (1992)Green, P.J., Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives: Journal of the Royal Statistical Society, Series (1984)McCullagh, P. and J. A. Nelder. Generalized Linear Models: Chapman & Hall (1990)Nelder, J.A and R.W.M. Wedderburn, Generalized Linear Models: K. R. Statist Soc A (1972), 135, part 3, p. 370

Read the original blog entry...

More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid

@ThingsExpo Stories
What does it look like when you have access to cloud infrastructure and platform under the same roof? Let’s talk about the different layers of Technology as a Service: who cares, what runs where, and how does it all fit together. In his session at 18th Cloud Expo, Phil Jackson, Lead Technology Evangelist at SoftLayer, an IBM company, spoke about the picture being painted by IBM Cloud and how the tools being crafted can help fill the gaps in your IT infrastructure.
SYS-CON Events announced today the Enterprise IoT Bootcamp, being held November 1-2, 2016, in conjunction with 19th Cloud Expo | @ThingsExpo at the Santa Clara Convention Center in Santa Clara, CA. Combined with real-world scenarios and use cases, the Enterprise IoT Bootcamp is not just based on presentations but with hands-on demos and detailed walkthroughs. We will introduce you to a variety of real world use cases prototyped using Arduino, Raspberry Pi, BeagleBone, Spark, and Intel Edison. Y...
19th Cloud Expo, taking place November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA, will feature technical sessions from a rock star conference faculty and the leading industry players in the world. Cloud computing is now being embraced by a majority of enterprises of all sizes. Yesterday's debate about public vs. private has transformed into the reality of hybrid cloud: a recent survey shows that 74% of enterprises have a hybrid cloud strategy. Meanwhile, 94% of enterpri...
Why do your mobile transformations need to happen today? Mobile is the strategy that enterprise transformation centers on to drive customer engagement. In his general session at @ThingsExpo, Roger Woods, Director, Mobile Product & Strategy – Adobe Marketing Cloud, covered key IoT and mobile trends that are forcing mobile transformation, key components of a solid mobile strategy and explored how brands are effectively driving mobile change throughout the enterprise.
Fact is, enterprises have significant legacy voice infrastructure that’s costly to replace with pure IP solutions. How can we bring this analog infrastructure into our shiny new cloud applications? There are proven methods to bind both legacy voice applications and traditional PSTN audio into cloud-based applications and services at a carrier scale. Some of the most successful implementations leverage WebRTC, WebSockets, SIP and other open source technologies. In his session at @ThingsExpo, Da...
If you had a chance to enter on the ground level of the largest e-commerce market in the world – would you? China is the world’s most populated country with the second largest economy and the world’s fastest growing market. It is estimated that by 2018 the Chinese market will be reaching over $30 billion in gaming revenue alone. Admittedly for a foreign company, doing business in China can be challenging. Often changing laws, administrative regulations and the often inscrutable Chinese Interne...
Cloud computing is being adopted in one form or another by 94% of enterprises today. Tens of billions of new devices are being connected to The Internet of Things. And Big Data is driving this bus. An exponential increase is expected in the amount of information being processed, managed, analyzed, and acted upon by enterprise IT. This amazing is not part of some distant future - it is happening today. One report shows a 650% increase in enterprise data by 2020. Other estimates are even higher....
An IoT product’s log files speak volumes about what’s happening with your products in the field, pinpointing current and potential issues, and enabling you to predict failures and save millions of dollars in inventory. But until recently, no one knew how to listen. In his session at @ThingsExpo, Dan Gettens, Chief Research Officer at OnProcess, will discuss recent research by Massachusetts Institute of Technology and OnProcess Technology, where MIT created a new, breakthrough analytics model f...
Personalization has long been the holy grail of marketing. Simply stated, communicate the most relevant offer to the right person and you will increase sales. To achieve this, you must understand the individual. Consequently, digital marketers developed many ways to gather and leverage customer information to deliver targeted experiences. In his session at @ThingsExpo, Lou Casal, Founder and Principal Consultant at Practicala, discussed how the Internet of Things (IoT) has accelerated our abil...
24Notion is full-service global creative digital marketing, technology and lifestyle agency that combines strategic ideas with customized tactical execution. With a broad understand of the art of traditional marketing, new media, communications and social influence, 24Notion uniquely understands how to connect your brand strategy with the right consumer. 24Notion ranked #12 on Corporate Social Responsibility - Book of List.
Adobe is changing the world though digital experiences. Adobe helps customers develop and deliver high-impact experiences that differentiate brands, build loyalty, and drive revenue across every screen, including smartphones, computers, tablets and TVs. Adobe content solutions are used daily by millions of companies worldwide-from publishers and broadcasters, to enterprises, marketing agencies and household-name brands. Building on its established design leadership, Adobe enables customers not o...
Everyone knows that truly innovative companies learn as they go along, pushing boundaries in response to market changes and demands. What's more of a mystery is how to balance innovation on a fresh platform built from scratch with the legacy tech stack, product suite and customers that continue to serve as the business' foundation. In his General Session at 19th Cloud Expo, Michael Chambliss, Head of Engineering at ReadyTalk, will discuss why and how ReadyTalk diverted from healthy revenue an...
Cognitive Computing is becoming the foundation for a new generation of solutions that have the potential to transform business. Unlike traditional approaches to building solutions, a cognitive computing approach allows the data to help determine the way applications are designed. This contrasts with conventional software development that begins with defining logic based on the current way a business operates. In her session at 18th Cloud Expo, Judith S. Hurwitz, President and CEO of Hurwitz & ...
The Internet of Things will challenge the status quo of how IT and development organizations operate. Or will it? Certainly the fog layer of IoT requires special insights about data ontology, security and transactional integrity. But the developmental challenges are the same: People, Process and Platform and how we integrate our thinking to solve complicated problems. In his session at 19th Cloud Expo, Craig Sproule, CEO of Metavine, will demonstrate how to move beyond today's coding paradigm ...
The Transparent Cloud-computing Consortium (abbreviation: T-Cloud Consortium) will conduct research activities into changes in the computing model as a result of collaboration between "device" and "cloud" and the creation of new value and markets through organic data processing High speed and high quality networks, and dramatic improvements in computer processing capabilities, have greatly changed the nature of applications and made the storing and processing of data on the network commonplace.
Digital transformation is too big and important for our future success to not understand the rules that apply to it. The first three rules for winning in this age of hyper-digital transformation are: Advantages in speed, analytics and operational tempos must be captured by implementing an optimized information logistics system (OILS) Real-time operational tempos (IT, people and business processes) must be achieved Businesses that can "analyze data and act and with speed" will dominate those t...
Almost two-thirds of companies either have or soon will have IoT as the backbone of their business in 2016. However, IoT is far more complex than most firms expected. How can you not get trapped in the pitfalls? In his session at @ThingsExpo, Tony Shan, a renowned visionary and thought leader, will introduce a holistic method of IoTification, which is the process of IoTifying the existing technology and business models to adopt and leverage IoT. He will drill down to the components in this fra...
As ridesharing competitors and enhanced services increase, notable changes are occurring in the transportation model. Despite the cost-effective means and flexibility of ridesharing, both drivers and users will need to be aware of the connected environment and how it will impact the ridesharing experience. In his session at @ThingsExpo, Timothy Evavold, Executive Director Automotive at Covisint, will discuss key challenges and solutions to powering a ride sharing and/or multimodal model in the a...
Internet of @ThingsExpo, taking place November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with the 19th International Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world and ThingsExpo Silicon Valley Call for Papers is now open.
I'm a lonely sensor. I spend all day telling the world how I'm feeling, but none of the other sensors seem to care. I want to be connected. I want to build relationships with other sensors to be more useful for my human. I want my human to understand that when my friends next door are too hot for a while, I'll soon be flaming. And when all my friends go outside without me, I may be left behind. Don't just log my data; use the relationship graph. In his session at @ThingsExpo, Ryan Boyd, Engi...