Contents
What are the features of the glmmtmb package?
glmmTMB is an R package built on the Template Model Builder automatic di erentiation engine, for tting generalized linear mixed models and exten-sions. (Not-yet-implemented features are denoted like this) response distributions: Gaussian, binomial, beta-binomial, Poisson, negative binomial (NB1 and NB2 parameterizations), Conway-Maxwell-
How can I deal with overdispersion in GLMMs?
A p-value < .05 indicates overdispersion. For Poisson models, the overdispersion test is based on the code from Gelman and Hill (2007), page 115. For merMod – and glmmTMB -objects, check_overdispersion () is based on the code in the GLMM FAQ , section How can I deal with overdispersion in GLMMs?.
How to interpret the overdispersion parameter of NB2?
In the output, I see the following line : Overdispersion parameter for nbinom2 family (): 9.28e+06. How do I interpret such a large overdispersion? Please help. actually means no overdispersion. This is the theta parameter of a NB2 model, see also What is theta in a negative binomial regression fitted with R?
How to interpret such a large overdispersion parameter?
In the output, I see the following line : Overdispersion parameter for nbinom2 family (): 9.28e+06. How do I interpret such a large overdispersion?
How to calculate the dispersion parameter in glmmtmb?
Denoting the variance as V, the dispersion parameter as phi=exp (eta) (where eta is the linear predictor from the dispersion model), and the predicted mean as mu : (from base R) phi is the shape parameter.
Is there a zero truncated version of nbinom2?
Zero-truncated version of nbinom2: variance expression from Shonkwiler 2016. Simulation code (for this and the other truncated count distributions) is taken from C. Geyer’s functions in the aster package; the algorithms are described in this vignette.
How to create a family function for glmmtmb?
If specified, the dispersion model uses a log link. Denoting the variance as V, the dispersion parameter as phi=exp (eta) (where eta is the linear predictor from the dispersion model), and the predicted mean as mu : (from base R) phi is the shape parameter. V=mu*phi