Jump to content

Comparison of general and generalized linear models

Add links
From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by 46.5.16.46 (talk) at 11:19, 21 August 2014 (exchanged ordinary least squares (OLS) for linear regression as OLS is an estimator and here we are looking for a special case of a model (linear regression model as special case)). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.
General linear model Generalized linear model
Typical estimation method Least squares, best linear unbiased prediction Maximum likelihood or Bayesian
Special cases ANOVA, ANCOVA, MANOVA, MANCOVA, linear regression, mixed model, t-test, F-test linear regression, logistic regression, Poisson regression, gamma regression[1]
Function in R lm() glm()
Function in Matlab mvregress() glmfit()
Procedure in SAS PROC GLM, PROC MIXED PROC GENMOD (PROC LOGISTIC for logistic regression only)
Command in Stata regress glm
Command in SPSS regression, glm genlin, logistic regression
Function in Mathematica LinearModelFit[] GeneralizedLinearModelFit[]
Command in EViews ls
  1. ^ McCullagh, Peter; Nelder, John (1989). Generalized Linear Models, Second Edition. Boca Raton: Chapman and Hall/CRC. ISBN 0-412-31760-5.

References

Stub icon

This statistics-related article is a stub. You can help Wikipedia by expanding it.

Retrieved from "https://en.wikipedia.org/w/index.php?title=Comparison_of_general_and_generalized_linear_models&oldid=622183222"