Additional families

Additional families compatible with sdmTMB().

Beta(link = "logit")

lognormal(link = "log")

nbinom2(link = "log")

nbinom1(link = "log")

truncated_nbinom2(link = "log")

truncated_nbinom1(link = "log")

student(link = "identity", df = 3)

tweedie(link = "log")

censored_poisson(link = "log")

Arguments

link

The link.

df

Student-t degrees of freedom fixed value parameter.

Details

The nbinom2 negative binomial parameterization is the NB2 where the variance grows quadratically with the mean (Hilbe 2011).

The nbinom1 negative binomial parameterization lets the variance grow linearly with the mean (Hilbe 2011).

The degrees of freedom parameter is currently not estimated and is fixed at df.

References

Hilbe, J. M. (2011). Negative binomial regression. Cambridge University Press.

Examples

Beta(link = "logit")
#> $family
#> [1] "Beta"
#> 
#> $link
#> [1] "logit"
#> 
#> $linkfun
#> function (mu) 
#> .Call(C_logit_link, mu)
#> <environment: namespace:stats>
#> 
#> $linkinv
#> function (eta) 
#> .Call(C_logit_linkinv, eta)
#> <environment: namespace:stats>
#> 
lognormal(link = "log")
#> $family
#> [1] "lognormal"
#> 
#> $link
#> [1] "log"
#> 
#> $linkfun
#> function (mu) 
#> log(mu)
#> <environment: namespace:stats>
#> 
#> $linkinv
#> function (eta) 
#> pmax(exp(eta), .Machine$double.eps)
#> <environment: namespace:stats>
#> 
nbinom2(link = "log")
#> $family
#> [1] "nbinom2"
#> 
#> $link
#> [1] "log"
#> 
#> $linkfun
#> function (mu) 
#> log(mu)
#> <environment: namespace:stats>
#> 
#> $linkinv
#> function (eta) 
#> pmax(exp(eta), .Machine$double.eps)
#> <environment: namespace:stats>
#> 
nbinom1(link = "log")
#> $family
#> [1] "nbinom1"
#> 
#> $link
#> [1] "log"
#> 
#> $linkfun
#> function (mu) 
#> log(mu)
#> <environment: namespace:stats>
#> 
#> $linkinv
#> function (eta) 
#> pmax(exp(eta), .Machine$double.eps)
#> <environment: namespace:stats>
#> 
truncated_nbinom2(link = "log")
#> $family
#> [1] "truncated_nbinom2"
#> 
#> $link
#> [1] "log"
#> 
#> $linkfun
#> function (mu) 
#> log(mu)
#> <environment: namespace:stats>
#> 
#> $linkinv
#> function (eta) 
#> pmax(exp(eta), .Machine$double.eps)
#> <environment: namespace:stats>
#> 
truncated_nbinom1(link = "log")
#> $family
#> [1] "truncated_nbinom1"
#> 
#> $link
#> [1] "log"
#> 
#> $linkfun
#> function (mu) 
#> log(mu)
#> <environment: namespace:stats>
#> 
#> $linkinv
#> function (eta) 
#> pmax(exp(eta), .Machine$double.eps)
#> <environment: namespace:stats>
#> 
student(link = "identity")
#> $family
#> [1] "student"
#> 
#> $link
#> [1] "identity"
#> 
#> $linkfun
#> function (mu) 
#> mu
#> <environment: namespace:stats>
#> 
#> $linkinv
#> function (eta) 
#> eta
#> <environment: namespace:stats>
#> 
#> $df
#> [1] 3
#> 
tweedie(link = "log")
#> $family
#> [1] "tweedie"
#> 
#> $link
#> [1] "log"
#> 
#> $linkfun
#> function (mu) 
#> log(mu)
#> <environment: namespace:stats>
#> 
#> $linkinv
#> function (eta) 
#> pmax(exp(eta), .Machine$double.eps)
#> <environment: namespace:stats>
#> 
censored_poisson(link = "log")
#> $family
#> [1] "censored_poisson"
#> 
#> $link
#> [1] "log"
#> 
#> $linkfun
#> function (mu) 
#> log(mu)
#> <environment: namespace:stats>
#> 
#> $linkinv
#> function (eta) 
#> pmax(exp(eta), .Machine$double.eps)
#> <environment: namespace:stats>
#>