nbinom2: Family functions for glmmTMB

View source: R/family.R

nbinom2R Documentation

Family functions for glmmTMB


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")

lognormal(link = "log")

ziGamma(link = "inverse")

t_family(link = "identity")

ordbeta(link = "logit")



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


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): constant V=\phi


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


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


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


Negative binomial distribution: linear parameterization (Hardin & Hilbe 2007). V=\mu(1+\phi)


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.


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.


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


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


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.


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)).


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


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


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(.))


  • 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")}.

glmmTMB documentation built on Oct. 7, 2023, 5:07 p.m.