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") ziGamma(link = "inverse")
link |
(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)
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.)
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^p. The power parameter is restricted to the interval 1<p<2
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 |
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.
Morris W (1997). "Disentangling Effects of Induced Plant Defenses and Food Quantity on Herbivores by Fitting Nonlinear Models." American Naturalist 150:299-327.
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. https://doi.org/10.1214/09-AOAS306.
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