get_prior_parameters() lists the parameters that custom priors can refer
to: the elements of par and theta passed to the custom and
custom_log_jacobian functions in sdmTMBpriors(), with their values. Use
it with a fitted model or one set up with do_fit = FALSE.
Arguments
- object
An
sdmTMB()model fit with the RTMB backend. Forget_prior_densities(), it must have custom priors.- random
Include the random effects (e.g., random field values)? There are often many of them.
Value
get_prior_parameters(): a data frame with one row per element:
expression: how to refer to the element in a custom prior function;list,name:"par"or"theta", and the element's name;label: the coefficient name, where available;value: the estimate (or starting value if the model isn't fit);status: forpar,"estimated","random", or"shared"(estimated jointly with other elements viamap);"derived"fortheta.
get_prior_densities(): a data frame with one row per term added to the
objective: type ("density", or "log_jacobian" if bayesian = TRUE),
term (the returned name, or its position), and log_density.
Details
get_prior_densities() evaluates the custom prior functions at the
estimated parameters, including random effects at their conditional modes,
and returns the log density of each term. Like the objective, it evaluates
custom_log_jacobian only if bayesian = TRUE.
par holds the raw parameters, with the names and shapes used internally.
Matrix columns are model components (e.g., the two parts of a delta model);
the second component's main effects are in b_j2. theta holds
natural-scale transformations of par, such as phi = exp(ln_phi) and
range = sqrt(8) / kappa.
Elements of par that are fixed, by map or because the model doesn't
use them, are left out, since a prior on them would have no effect. All elements of theta
are listed, including transformations of parameters this model doesn't use
(e.g., tweedie_p in a model without a Tweedie family). Check that the
par elements underlying a theta element are listed before putting a
prior on it. Standard deviations of random fields the model doesn't
include (e.g., sigma_O without a spatial field) are zero.
These names and shapes are internal and could change in future versions of
sdmTMB. Check them with get_prior_parameters() when writing custom priors.
Examples
fit <- sdmTMB(density ~ depth_scaled, data = pcod_2011,
family = tweedie(), mesh = pcod_mesh_2011,
control = sdmTMBcontrol(backend = "rtmb"),
priors = sdmTMBpriors(custom = function(par, theta) {
c(depth = RTMB::dnorm(par$b_j[2], 0, 1, log = TRUE))
}))
get_prior_parameters(fit)
#> expression list name label value status
#> 1 par$b_j[1] par b_j (Intercept) 2.8939851 estimated
#> 2 par$b_j[2] par b_j depth_scaled -0.6321196 estimated
#> 3 par$ln_tau_O[1] par ln_tau_O <NA> 0.2689947 estimated
#> 4 par$ln_kappa[1, 1] par ln_kappa <NA> -2.2958680 shared
#> 5 par$ln_kappa[2, 1] par ln_kappa <NA> -2.2958680 shared
#> 6 par$thetaf[1] par thetaf <NA> 0.3632222 estimated
#> 7 par$ln_phi[1] par ln_phi <NA> 2.7275598 estimated
#> 8 theta$kappa[1, 1] theta kappa <NA> 0.1006740 derived
#> 9 theta$kappa[2, 1] theta kappa <NA> 0.1006740 derived
#> 10 theta$range[1, 1] theta range <NA> 28.0949210 derived
#> 11 theta$range[2, 1] theta range <NA> 28.0949210 derived
#> 12 theta$sigma_O[1, 1] theta sigma_O <NA> 2.1411888 derived
#> 13 theta$log_sigma_O[1, 1] theta log_sigma_O <NA> 0.7613612 derived
#> 14 theta$sigma_E[1, 1] theta sigma_E <NA> 0.0000000 derived
#> 15 theta$log_sigma_E[1, 1] theta log_sigma_E <NA> 0.0000000 derived
#> 16 theta$rho[1] theta rho <NA> 0.0000000 derived
#> 17 theta$sigma_V[1, 1] theta sigma_V <NA> 0.0000000 derived
#> 18 theta$rho_time[1, 1] theta rho_time <NA> 0.0000000 derived
#> 19 theta$phi[1] theta phi <NA> 15.2955166 derived
#> 20 theta$tweedie_p[1] theta tweedie_p <NA> 1.5898202 derived
#> 21 theta$p_extreme[1] theta p_extreme <NA> 0.5000000 derived
#> 22 theta$mix_ratio[1] theta mix_ratio <NA> 1.3678794 derived
get_prior_densities(fit)
#> type term log_density
#> 1 density depth -1.118726