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A generalization of linear regression allowing for nonlinear relationships via a "link function" and for the variance of the response to depend on the predicted value. (Not to be confused with "general linear model" which extends the ordinary linear model to general covariance structure and multivariate response.)

11
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In my opinion, it assumes that the errors lie on a family of gamma distributions, with the same shapes, and with the scales changing according the related formula. But it is difficult to do model dia …
answered Aug 16 '13 by Vincent
1
vote
It is quite interesting to hear using glm to replace the fitdistr. But at least the code has some problems, which you may have found from the totally different AIC or likelihood. The glm family distr …
answered May 6 '14 by Vincent
4
votes
For those initial factor predictors, it is arguable whether those insignificant levels should be "merged". Note that your approach seems to be simply dropping those insignificant levels, this approach …
answered May 29 '14 by Vincent
4
votes
$\newcommand{\variable}{\rm variable}$The link function is link to parameter of the distribution (in this example is $p$ of Bernoulli distribution) to the linear score $\eta$ (in this example is $b_0+ …
answered Aug 26 '15 by Vincent
1
vote
According to the Frequentists' theory and MLE, the model and other following statistical tests only work correctly when it follows the real underlying data generating distribution. If the data is sam …
answered Sep 5 by Vincent
5
votes
The summary.glm() for glm is the Wald test for that level of factor (compared to the base level), while the anova.glm() is the Chisquared-test (based on deviance) for the whole factor variable. So the …
answered Aug 21 '13 by Vincent