Family functions for glmmTMB
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")
(character) link function for the conditional mean ("log", "logit", "probit", "inverse", "cloglog", or "identity")
returns a list with (at least) components
length-1 character vector giving the family name
length-1 character vector specifying the link function
a function of either 1 (mean) or 2 (mean and dispersion
parameter) arguments giving a value proportional to the
predicted variance (scaled by sigma(.))
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:
(from base R): constant variance=phi
(from base R) phi is the shape parameter, i.e variance=mu*phi
variance increases quadratically with the mean (Hardin & Hilbe 2007), i.e. variance=mu*(1+mu/phi)
variance increases linearly with the mean (Hardin & Hilbe 2007), i.e. variance=mu*(1+phi)
is the Conway-Maxwell Poisson parameterized with the exact mean which differs from the COMPoissonReg package (Sellers & Lotze 2015)
is the generalized Poisson distribution
follows the parameterization of Ferrari and Cribari-Neto (2004) and the betareg package,
i.e. variance=mu*(1-mu)
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