set_disp: Specify Prior for Dispersion or Standard Deviation

In bage: Bayesian Estimation and Forecasting of Age-Specific Rates

Specify Prior for Dispersion or Standard Deviation

Description

Specify the mean of prior for the dispersion parameter (in Poisson and binomial models) or the standard deviation parameter (in normal models.)

Usage

set_disp(mod, mean = 1)

Arguments

mod	An object of class "bage_mod", created with mod_pois(), mod_binom(), or mod_norm().
mean	Mean value for the exponential prior. In Poisson and binomial models, can be set to 0. Default is 1.

Details

The dispersion or mean parameter has an exponential distribution with mean \mu,

p(\xi) = \frac{1}{\mu}\exp\left(\frac{-\xi}{\mu}\right).

By default \mu equals 1.

In Poisson and binomial models, mean can be set to 0, implying that the dispersion term is also 0. In normal models, mean must be non-negative.

If set_disp() is applied to a fitted model, set_disp() unfits the model, deleting existing estimates.

Value

A bage_mod object

See Also

• mod_pois(), mod_binom(), mod_norm() Specify a model for rates, probabilities, or means

• set_prior() Specify prior for a term

• set_n_draw() Specify the number of draws

• is_fitted() Test whether a model is fitted

Examples

mod <- mod_pois(injuries ~ age:sex + ethnicity + year,
                data = nzl_injuries,
                exposure = popn)
mod
mod |> set_disp(mean = 0.1)
mod |> set_disp(mean = 0)

bage documentation built on Nov. 5, 2025, 5:33 p.m.