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")

lognormal(link = "log")

ziGamma(link = "inverse")

t_family(link = "identity")

ordbeta(link = "logit")

Arguments

link

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

Details

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:

gaussian

(from base R): constant V=\phi^2

Gamma

(from base R) phi is the shape parameter. V=\mu\phi

ziGamma

a modified version of Gamma that skips checks for zero values, allowing it to be used to fit hurdle-Gamma models

nbinom2

Negative binomial distribution: quadratic parameterization (Hardin & Hilbe 2007). V=\mu(1+\mu/\phi) = \mu+\mu^2/\phi.

nbinom1

Negative binomial distribution: linear parameterization (Hardin & Hilbe 2007). V=\mu(1+\phi). Note that the phi parameter has opposite meanings in the nbinom1 and nbinom2 families. In nbinom1 overdispersion increases with increasing phi (the Poisson limit is phi=0); in nbinom2 overdispersion decreases with increasing phi (the Poisson limit is reached as phi goes to infinity).

truncated_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.

compois

Conway-Maxwell Poisson distribution: parameterized with the exact mean (Huang 2017), which differs from the parameterization used in the COMPoissonReg package (Sellers & Shmueli 2010, Sellers & Lotze 2015). V=\mu\phi.

genpois

Generalized Poisson distribution (Consul & Famoye 1992). V=\mu\exp(\eta). (Note that Consul & Famoye (1992) define \phi differently.) Our implementation is taken from the HMMpa package, based on Joe and Zhu (2005) and implemented by Vitali Witowski.

beta

Beta distribution: parameterization of Ferrari and Cribari-Neto (2004) and the betareg package (Cribari-Neto and Zeileis 2010); V=\mu(1-\mu)/(\phi+1)

betabinomial

Beta-binomial distribution: parameterized according to Morris (1997). V=\mu(1-\mu)(n(\phi+n)/(\phi+1))

tweedie

Tweedie distribution: V=\phi\mu^power. The power parameter is restricted to the interval 1<power<2. Code taken from the tweedie package, written by Peter Dunn.

t_family

Student-t distribution with adjustable scale and location parameters (also called a Pearson type VII distribution). The shape (degrees of freedom parameter) is fitted with a log link; it may be often be useful to fix the shape parameter using start = list(psi = log(fixed_df)), map = list(psi = factor(NA)).

ordbeta

Ordered beta regression from Kubinec (2022); fits continuous (e.g. proportion) data in the closed interval [0,1].

lognormal

Log-normal, parameterized by the mean and standard deviation on the data scale

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 a value proportional to the predicted variance (scaled by sigma(.))

References

[Package glmmTMB version 1.1.9 Index]