Display the default prior specifications used for each model family. Useful for understanding what priors are applied before fitting and as a starting point for customization.
Arguments
- family
A tulpaRatio family object (e.g.,
ratiod_negbin_negbin()). If NULL (default), shows defaults for all families.- spatial
Logical; if TRUE, include spatial priors. Default FALSE.
- temporal
Logical; if TRUE, include temporal priors. Default FALSE.
Value
Invisibly returns a ratiod_priors object with the defaults.
Primarily called for its side effect of printing.
Details
Default priors in tulpaRatio follow these principles:
Fixed effects (beta): Normal(0, 2.5) - weakly informative, allows coefficients roughly in [-5, 5] on the link scale.
Random effect SD (sigma): PC prior with P(sigma > 1) = 0.01 - favors simpler models with smaller variance components.
Overdispersion (phi): PC prior with P(phi > 10) = 0.01 - regularizes toward Poisson (phi -> Inf means less overdispersion in NB2).
Temporal correlation (rho): Beta(2, 2) - symmetric prior centered at 0.5, appropriate for AR(1) correlation.
Spatial mixing (rho_spatial): Beta(1, 1) = Uniform(0, 1) - no prior preference for structured vs. unstructured spatial variation.
See also
ratiod_priors() for creating custom priors
Examples
# Show defaults for negbin_negbin family
priors_default(ratiod_negbin_negbin())
#> Default priors for negbin_negbin family
#> =====================================
#>
#> Fixed effects (beta):
#> Normal(0.00, 2.50)
#> Used for: All regression coefficients in numerator and denominator
#>
#> Random effect SD (sigma):
#> PC prior: P(x > 1.00) = 0.010
#> => Exponential(4.605)
#> Used for: Standard deviation of group-level effects
#>
#> Overdispersion (phi):
#> PC prior: P(x > 10.00) = 0.010
#> => Exponential(0.461)
#> Used for: NB2 size parameter (larger = less overdispersion)
#>
#> To customize, use ratiod_priors():
#> priors <- ratiod_priors(
#> beta = prior_normal(0, 1),
#> sigma = prior_pc(U = 0.5, alpha = 0.01)
#> )
# Show defaults for binomial family
priors_default(ratiod_binomial())
#> Default priors for binomial_fixed family
#> ======================================
#>
#> Fixed effects (beta):
#> Normal(0.00, 2.50)
#> Used for: All regression coefficients in numerator and denominator
#>
#> Random effect SD (sigma):
#> PC prior: P(x > 1.00) = 0.010
#> => Exponential(4.605)
#> Used for: Standard deviation of group-level effects
#>
#> To customize, use ratiod_priors():
#> priors <- ratiod_priors(
#> beta = prior_normal(0, 1),
#> sigma = prior_pc(U = 0.5, alpha = 0.01)
#> )
# Show defaults including spatial parameters
priors_default(ratiod_negbin_negbin(), spatial = TRUE)
#> Default priors for negbin_negbin family
#> =====================================
#>
#> Fixed effects (beta):
#> Normal(0.00, 2.50)
#> Used for: All regression coefficients in numerator and denominator
#>
#> Random effect SD (sigma):
#> PC prior: P(x > 1.00) = 0.010
#> => Exponential(4.605)
#> Used for: Standard deviation of group-level effects
#>
#> Overdispersion (phi):
#> PC prior: P(x > 10.00) = 0.010
#> => Exponential(0.461)
#> Used for: NB2 size parameter (larger = less overdispersion)
#>
#> Spatial mixing (rho_spatial):
#> Beta(1.00, 1.00) [mean = 0.50]
#> Used for: BYM2 mixing proportion (structured vs. unstructured)
#>
#> To customize, use ratiod_priors():
#> priors <- ratiod_priors(
#> beta = prior_normal(0, 1),
#> sigma = prior_pc(U = 0.5, alpha = 0.01)
#> )
# Show defaults for all families
priors_default()
#> Default priors for ratio models
#> ================================
#>
#> These defaults apply to all families unless overridden.
#>
#> Fixed effects (beta):
#> Normal(0, 2.5)
#> Interpretation: Coefficients roughly in [-5, 5] on link scale
#> Customization: prior_normal(mean, sd)
#>
#> Random effect SD (sigma):
#> PC prior: P(sigma > 1) = 0.01
#> Interpretation: Favors smaller variance components
#> Customization: prior_pc(U, alpha) or prior_half_normal(sd)
#>
#> Overdispersion (phi) [negbin/poisson_gamma only]:
#> PC prior: P(phi > 10) = 0.01
#> Interpretation: Regularizes toward Poisson
#> Customization: prior_pc(U, alpha) or prior_gamma(shape, rate)
#>
#> Family-specific notes:
#> negbin_negbin: Uses phi for both numerator and denominator
#> binomial: No overdispersion parameter (unless beta_binomial)
#> poisson_gamma: Uses phi for gamma shape parameter
# Use as starting point for customization
my_priors <- priors_default(ratiod_poisson_gamma())
#> Default priors for poisson_gamma family
#> =====================================
#>
#> Fixed effects (beta):
#> Normal(0.00, 2.50)
#> Used for: All regression coefficients in numerator and denominator
#>
#> Random effect SD (sigma):
#> PC prior: P(x > 1.00) = 0.010
#> => Exponential(4.605)
#> Used for: Standard deviation of group-level effects
#>
#> Overdispersion (phi):
#> PC prior: P(x > 10.00) = 0.010
#> => Exponential(0.461)
#> Used for: Gamma shape parameter for effort/exposure
#>
#> To customize, use ratiod_priors():
#> priors <- ratiod_priors(
#> beta = prior_normal(0, 1),
#> sigma = prior_pc(U = 0.5, alpha = 0.01)
#> )
my_priors$beta <- prior_normal(0, 1) # Tighter prior on fixed effects