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Adding a link and specific section number to the referred and vetted discussion in MASS
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edit approved Aug 28, 2015 at 15:09
swihart
edit approved Aug 28, 2015 at 15:09
swihart
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You are right, these data might likely be overdispersed. Quasipoisson is a remedy: It estimates a scale parameter as well (which is fixed for poisson models as the variance is also the mean) and will provide better fit. However, it is no longer maximum likelihood what you are then doing, and certain model tests and indices can't be used. A good discussion can be found in Venables and Ripley, Modern Applied Statistics with S (Section 7.5).

An alternative is to use a negative binomial model, e.g. the glm.nb() function in package MASS.

You are right, these data might likely be overdispersed. Quasipoisson is a remedy: It estimates a scale parameter as well (which is fixed for poisson models as the variance is also the mean) and will provide better fit. However, it is no longer maximum likelihood what you are then doing, and certain model tests and indices can't be used. A good discussion can be found in Venables and Ripley, Modern Applied Statistics with S.

An alternative is to use a negative binomial model, e.g. the glm.nb() function in package MASS.

You are right, these data might likely be overdispersed. Quasipoisson is a remedy: It estimates a scale parameter as well (which is fixed for poisson models as the variance is also the mean) and will provide better fit. However, it is no longer maximum likelihood what you are then doing, and certain model tests and indices can't be used. A good discussion can be found in Venables and Ripley, Modern Applied Statistics with S (Section 7.5).

An alternative is to use a negative binomial model, e.g. the glm.nb() function in package MASS.

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Momo
Momo
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You are right, these data might likely be overdispersed. Quasipoisson is a remedy: It estimates a scale parameter as well (which is fixed for poisson models as the variance is also the mean) and will provide better fit. However, it is no longer maximum likelihood what you are then doing, and certain model tests and indices can't be used. A good discussion can be found in Venables and Ripley, Modern Applied Statistics with S.

An alternative is to use a negative binomial model, e.g. the glm.nb() function in package MASS.