R: Family functions for glmmTMB

nbinom2 {glmmTMB} | R Documentation

## Family functions for glmmTMB

### Description

Family functions for glmmTMB

### Usage

    nbinom2(link = "log")

    nbinom1(link = "log")

    compois(link = "log")

    truncated_compois(link = "log")

    genpois(link = "log")

    truncated_genpois(link = "log")

    truncated_poisson(link = "log")

    truncated_nbinom2(link = "log")

    truncated_nbinom1(link = "log")

    beta_family(link = "logit")

    betabinomial(link = "logit")

    tweedie(link = "log")

### Arguments

`link` | (character) link function for the conditional mean ("log", "logit", "probit", "inverse", "cloglog", or "identity")

### Details

If specified, the dispersion model uses a log link. Denoting the dispersion parameter as phi=exp(eta) (where eta is the linear predictor from the dispersion model) and the predicted mean as mu:

gaussian

  (from base R): constant variance=phi

Gamma

  (from base R) phi is the shape parameter, i.e variance=mu*phi

nbinom2

  variance increases quadratically with the mean (Hardin & Hilbe 2007), i.e. variance=mu*(1+mu/phi)

nbinom1

  variance increases linearly with the mean (Hardin & Hilbe 2007), i.e. variance=mu*(1+phi)

compois

  is the Conway-Maxwell Poisson parameterized with the exact mean which differs from the COMPoissonReg package (Sellers & Lotze 2015)

genpois

  is the generalized Poisson distribution

beta

  follows the parameterization of Ferrari and Cribari-Neto (2004) and the `betareg` package, i.e. variance=mu*(1-mu)/(1+phi)

### Value

returns a list with (at least) components

`family` | length-1 character vector giving the family name
`link` | length-1 character vector specifying the link function
`variance` | a function of either 1 (mean) or 2 (mean and dispersion parameter) arguments giving the predicted variance

### References

- Ferrari SLP, Cribari-Neto F (2004). "Beta Regression for Modelling Rates and Proportions." J. Appl. Stat. 31(7), 799-815.

- Hardin JW & Hilbe JM (2007). "Generalized linear models and extensions." Stata Press.

- Sellers K & Lotze T (2015). "COMPoissonReg: Conway-Maxwell Poisson (COM-Poisson) Regression". R package version 0.3.5. https://CRAN.R-project.org/package=COMPoissonReg

[Package glmmTMB version 0.2.2.0 Index]