Contents
What is GLM NB?
Description. A modification of the system function glm() to include estimation of the additional parameter, theta , for a Negative Binomial generalized linear model.
What package is GLM NB in?
In R (R Development Core Team 2008), GLMs are provided by the model fitting functions glm() (Chambers and Hastie 1992) in the stats package and glm. nb() in the MASS package Page 2 2 Regression Models for Count Data in R (Venables and Ripley 2002) along with associated methods for diagnostics and inference.
When should I use negative binomial regression?
Negative binomial regression – Negative binomial regression can be used for over-dispersed count data, that is when the conditional variance exceeds the conditional mean.
What is Theta in GLM NB?
The R glm and glm. nb θ is a dispersion parameter, or ancillary parameter. I called it the heterogeneity parameter in the first edition of my book, Negative Binomial Regression (2007, Cambridge University Press), but call it the dispersion parameter in my 2011 second edition.
What is overdispersed count data?
In statistics, overdispersion is the presence of greater variability (statistical dispersion) in a data set than would be expected based on a given statistical model. Conversely, underdispersion means that there was less variation in the data than predicted.
How is the GLM fitted for fixed means?
For given theta the GLM is fitted using the same process as used by glm (). For fixed means the theta parameter is estimated using score and information iterations. The two are alternated until convergence of both.
How is the link function fitted in GLM?
The link function. Currently must be one of log, sqrt or identity . An alternating iteration process is used. For given theta the GLM is fitted using the same process as used by glm (). For fixed means the theta parameter is estimated using score and information iterations.
How is the GLM fitted for a given theta parameter?
An alternating iteration process is used. For given theta the GLM is fitted using the same process as used by glm (). For fixed means the theta parameter is estimated using score and information iterations. The two are alternated until convergence of both.
Where does the dispersion parameter come from in GLM?
But I think you would like to know where dispersion parameter comes from: The dispersion parameter ϕ here is simply fixed at 1 (because it is a Negative Binomial GLM with known shape parameter that is used in the second stage). Thanks for contributing an answer to Cross Validated!