GLIM (software) explained

GLIM (an acronym for Generalized Linear Interactive Modelling) is a statistical software program for fitting generalized linear models (GLMs). It was developed by the Royal Statistical Society's Working Party on Statistical Computing(later renamed the GLIM Working Party),[1] chaired initially by John Nelder.[2] It was first released in 1974with the last major release, GLIM4, in 1993.[3] GLIM was distributed by the Numerical Algorithms Group (NAG).[4]

GLIM was notable for being the first package capable of fitting a wide range of generalized linear models in a unified framework, and for encouraging an interactive, iterative approach to statistical modelling.[5] GLIM used a command-line interface and allowed users to define their own macros. Many articles in academic journals were written about the use of GLIM.[6] [7] [8] [9] [10] [11] [12] Two GLIM conferences were held in London (1982) and Lancaster (1985) and the Statistical Modelling Society, with its annual workshops, grew out of them. GLIM was reviewed in The American Statistician in 1994, along with other software for fitting generalized linear models.[13]

The GLIMPSE system was later developed to provide a knowledge based front-end for GLIM.[14]

GLIM is no longer actively developed or distributed.

Books

. GLIM: an introduction. 1988. Clarendon Press. 978-0-19-852213-3. Michael Healy (statistician).

Notes and References

  1. Web site: Royal Statistical Society webpage on Working Parties . 2007-12-18 . bot: unknown . https://web.archive.org/web/20070221234751/http://www.rss.org.uk/main.asp?page=2128 . February 21, 2007 .
  2. Nelder . John . John Nelder . 1975 . Announcement by the Working Party on Statistical Computing: GLIM (Generalized Linear Interactive Modelling Program) . . 24 . 2 . 259–261 . 2346575 .
  3. Book: Francis, Brian . The GLIM System: Release 4 Manual . Mick Green . Clive Payne . 1993 . Clarendon Press . Oxford . 0-19-852231-2.
  4. Web site: Generalized Linear Interactive Modeling Package (GLIM) . 2007-12-18 . bot: unknown . https://web.archive.org/web/20101012041544/http://www.nag.co.uk/stats/GDGE_soft.asp . 12 October 2010 .
  5. Book: Aitkin, Murray . Statistical Modelling in GLIM . Dorothy Anderson . Brian Francis . John Hinde . 1989 . Oxford University Press . Oxford . 0-19-852203-7 .
  6. Wacholder. Sholom. Binomial regression in GLIM: Estimating risk ratios and risk differences. American Journal of Epidemiology. 1986. 123. 1. 174–184 . 3509965.
  7. Aitken. Murray. Clayton, David . David Clayton . The Fitting of Exponential, Weibull and Extreme Value Distributions to Complex Censored Survival Data Using GLIM. Journal of the Royal Statistical Society, Series C. 1980. 29. 2. 156–163. 2986301.
  8. Aitkin. Murray. Modelling Variance Heterogeneity in Normal Regression Using GLIM. Journal of the Royal Statistical Society, Series C. 1987. 36. 3. 2347792.
  9. Whitehead. John. Fitting Cox's Regression Model to Survival Data using GLIM. . 1980. 29. 3. 2346901.
  10. Berman, Mark . Turner, Rolf T.. Approximating Point Process Likelihoods with GLIM. Journal of the Royal Statistical Society, Series C. 1992. 41. 1. 31–38. 2347614.
  11. Decarli, A.. La Vecchia, C.. Age, period and cohort models: review of knowledge and implementation in GLIM. Rev. Stat. App.. 1987. 20. 397–409.
  12. 10.2307/1403047. Jørgensen. Bent. The Delta Algorithm and GLIM . International Statistical Review / Revue Internationale de Statistique. 1984. 52. 3. 283–300. 1403047.
  13. 10.2307/2684732. Hilbe. Joseph. Joseph Hilbe. Review: Generalized Linear Models . The American Statistician. 1994. 48. 3. 255–265. 2684732. 1308.2408.
  14. 1. Knowledge-Based Systems. 173. 1988. 10.1016/0950-7051(88)90075-5. Wolstenholme. GLIMPSE: a knowledge-based front end for statistical analysis . D.. Obrien . C. . J.. Nelder. 3.

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