nbinom2: Family functions for glmmTMB
In glmmTMB: Generalized Linear Mixed Models using Template Model Builder

nbinom2

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
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)
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

Consul PC & Famoye F (1992). "Generalized Poisson regression model." Communications in Statistics: Theory and Methods 21:89–109.
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.
Huang A (2017). "Mean-parametrized Conway–Maxwell–Poisson regression models for dispersed counts." Statistical Modelling 17(6), 1-22.
Joe H, Zhu R (2005). "Generalized Poisson Distribution: The Property of Mixture of Poisson and Comparison with Negative Binomial Distribution." Biometrical Journal 47(2): 219–29. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/bimj.200410102")}.
Morris W (1997). "Disentangling Effects of Induced Plant Defenses and Food Quantity on Herbivores by Fitting Nonlinear Models." American Naturalist 150:299-327.
Kubinec R (2022). "Ordered Beta Regression: A Parsimonious, Well-Fitting Model for Continuous Data with Lower and Upper Bounds." Political Analysis. doi:10.1017/pan.2022.20.
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
Sellers K & Shmueli G (2010) "A Flexible Regression Model for Count Data." Annals of Applied Statistics 4(2), 943–61. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/09-AOAS306")}.
Shonkwiler, J. S. (2016). "Variance of the truncated negative binomial distribution." Journal of Econometrics 195(2), 209–210. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jeconom.2016.09.002")}.