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Show default priors for a tulpa family

Source: R/priors.R
priors_default.Rd

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.

Usage

priors_default(family = NULL, spatial = FALSE, temporal = FALSE)

Arguments

family

A tulpa family object (e.g. a ratio family constructor from a model package such as tulpaRatio). 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 tulpa_priors object with the defaults. Primarily called for its side effect of printing.

Details

Default priors in tulpa 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 on the NB2 size phi (larger phi is less overdispersion; phi -> Inf is the Poisson limit). The default keeps phi finite, allowing overdispersion while penalising extreme values.

  • 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

tulpa_priors() for creating custom priors

Examples

# Defaults for all families (no family argument)
priors_default()

# Family-specific defaults take a tulpa_family object. Model packages
# (e.g. tulpaRatio) register rich families; a minimal one is enough here.
fam <- tulpa_family(
  name = "poisson_gamma",
  simulate_fn = function(eta, params, n_obs, ...) rpois(n_obs, exp(eta[[1]]))
)
priors_default(fam)

# Including spatial parameters
priors_default(fam, spatial = TRUE)

# Use as a starting point for customization
my_priors <- priors_default(fam)
my_priors$beta <- prior_normal(0, 1)  # Tighter prior on fixed effects