> Hmmm. Thinking some more, I might not have answered your (unstated)
> question. What do your mean by GLM?
>
> => I meant generalized linear model as well. Thanks for the references.
The first one was mentioned first in my life time after keeping asking
the same question.
> I mean the Generalized Linear Model, not the General Linear Model. The
> Generalized one allows for non-normal responses and different
> mean-variance relationships and is the GLM of Nelder and Wedderburn
> (1972, J. Royal Statistical Society, Series A, 135(3),370-384) and the
> monograph by McCullagh and Wedderburn (1989, Generalized Linear Models,
> Chapman & Hall/CRC). The R function glm() fits these kinds of model.
>
> The General Linear Model (IIRC) was the linking of linear regression and
> anova into a single entity. The R function lm() fits these kinds of
> models.
>
> The linear model is a special case of the Generalized Linear Model when
> the Gaussian error is used with the identity link function. Hence a
> Gaussian GLM (my GLM) with the identity link fitted by glm() will give
> the same results as lm(), but it will do so in a very inefficient
> manner. As this was what your code was doing I suggested using lm()
> instead.
>
=> Thanks for the important suggestion.
I tried both lm and glm until now but no one commented on the method.
>
> Elaine
>
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
