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Comparison of general and generalized linear models

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This is an old revision of this page, as edited by Brgray3 (talk | contribs) at 14:59, 2 February 2016 (added PROC GLIMMIX under generalized LM, and modified parenthetic remark on PROC LOGISTIC from "(for logistic regression only)" to "(for regression with categorical variables)"). 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 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 GLIMMIX, PROC LOGISTIC (for regression with categorical variables)
Command in Stata regress glm
Command in SPSS regression, glm genlin, logistic regression
Function in Wolfram Language & Mathematica LinearModelFit[][2] GeneralizedLinearModelFit[][3]
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.
  2. ^ LinearModelFit, Wolfram Language Documentation Center.
  3. ^ GeneralizedLinearModelFit, Wolfram Language Documentation Center.

References


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