Families | R Documentation |
Additional families compatible with sdmTMB()
.
Beta(link = "logit")
lognormal(link = "log")
gengamma(link = "log")
gamma_mix(link = "log")
lognormal_mix(link = "log")
nbinom2_mix(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")
delta_gamma(link1, link2 = "log", type = c("standard", "poisson-link"))
delta_gamma_mix(link1 = "logit", link2 = "log")
delta_gengamma(link1, link2 = "log", type = c("standard", "poisson-link"))
delta_lognormal(link1, link2 = "log", type = c("standard", "poisson-link"))
delta_lognormal_mix(link1, link2 = "log", type = c("standard", "poisson-link"))
delta_truncated_nbinom2(link1 = "logit", link2 = "log")
delta_truncated_nbinom1(link1 = "logit", link2 = "log")
delta_poisson_link_gamma(link1 = "log", link2 = "log")
delta_poisson_link_lognormal(link1 = "log", link2 = "log")
delta_beta(link1 = "logit", link2 = "logit")
link |
Link. |
df |
Student-t degrees of freedom fixed value parameter. |
link1 |
Link for first part of delta/hurdle model. Defaults to |
link2 |
Link for second part of delta/hurdle model. |
type |
Delta/hurdle family type. |
delta_poisson_link_gamma()
and delta_poisson_link_lognormal()
have been
deprecated in favour of delta_gamma(type = "poisson-link")
and
delta_lognormal(type = "poisson-link")
.
The gengamma()
family was implemented by J.T. Thorson and uses the Prentice
(1974) parameterization such that the lognormal occurs as the internal
parameter gengamma_Q
(reported in print()
or summary()
as
"Generalized gamma Q") approaches 0. If Q matches phi
the distribution
should be the gamma.
The families ending in _mix()
are 2-component mixtures where each
distribution has its own mean but a shared scale parameter.
(Thorson et al. 2011). See the model-description vignette for details.
The parameter plogis(log_p_mix)
is the probability of the extreme (larger)
mean and exp(log_ratio_mix) + 1
is the ratio of the larger extreme
mean to the "regular" mean. You can see these parameters in
model$sd_report
.
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).
For student()
, the degrees of freedom parameter is currently not estimated and is fixed at df
.
A list with elements common to standard R family objects including family
,
link
, linkfun
, and linkinv
. Delta/hurdle model families also have
elements delta
(logical) and type
(standard vs. Poisson-link).
Generalized gamma family:
Prentice, R.L. 1974. A log gamma model and its maximum likelihood estimation. Biometrika 61(3): 539–544. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biomet/61.3.539")}
Stacy, E.W. 1962. A Generalization of the Gamma Distribution. The Annals of Mathematical Statistics 33(3): 1187–1192. Institute of Mathematical Statistics.
Families ending in _mix()
:
Thorson, J.T., Stewart, I.J., and Punt, A.E. 2011. Accounting for fish shoals in single- and multi-species survey data using mixture distribution models. Can. J. Fish. Aquat. Sci. 68(9): 1681–1693. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1139/f2011-086")}.
Negative binomial families:
Hilbe, J. M. 2011. Negative binomial regression. Cambridge University Press.
Poisson-link delta families:
Thorson, J.T. 2018. Three problems with the conventional delta-model for biomass sampling data, and a computationally efficient alternative. Canadian Journal of Fisheries and Aquatic Sciences, 75(9), 1369-1382. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1139/cjfas-2017-0266")}
Beta(link = "logit")
lognormal(link = "log")
gengamma(link = "log")
gamma_mix(link = "log")
lognormal_mix(link = "log")
nbinom2_mix(link = "log")
nbinom2(link = "log")
nbinom1(link = "log")
truncated_nbinom2(link = "log")
truncated_nbinom1(link = "log")
student(link = "identity")
tweedie(link = "log")
censored_poisson(link = "log")
delta_gamma()
delta_gamma_mix()
delta_gengamma()
delta_lognormal()
delta_lognormal_mix()
delta_truncated_nbinom2()
delta_truncated_nbinom1()
delta_beta()
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