components.bage_mod: Extract Values for Hyper-Parameters

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

Extract Values for Hyper-Parameters

Description

Extract values for hyper-parameters from a model object. Hyper-parameters include

• main effects and interactions,

• dispersion,

• trends, seasonal effects, errors,

• SVD, spline, and covariate coefficients,

• standard deviations, correlation coefficients.

Usage

## S3 method for class 'bage_mod'
components(object, quiet = FALSE, original_scale = FALSE, ...)

Arguments

object	Object of class "bage_mod", typically created with mod_pois(), mod_binom(), or mod_norm().
quiet	Whether to suppress messages. Default is FALSE.
original_scale	Whether values for "effect", "trend", "season", "error" and "disp" components from a normal model are on the original scale or the transformed scale. Default is FALSE.
...	Unused. Included for generic consistency only.

Value

A tibble with four columns columns:

The return value contains the following columns:

• term Model term that the hyper-parameter belongs to.

• component Component within term.

• level Element within component .

• .fitted An rvec containing draws from the posterior distribution.

Fitted vs unfitted models

components() is typically called on a fitted model. In this case, the values returned are draws from the joint posterior distribution for the hyper-parameters in the model.

components() can, however, be called on an unfitted model. In this case, the values returned are draws from the joint prior distribution. In other words, the values incorporate model priors, and any exposure, size, or weights argument, but not observed outcomes.

Scaling and Normal models

Internally, models created with mod_norm() are fitted using transformed versions of the outcome and weights variables. By default, when components() is used with these models, it returns values for .fitted that are based on the transformed versions. To instead obtain values for "effect", "trend", "season", "error" and "disp" that are based on the untransformed versions, set original_scale to TRUE.

See Also

• augment() Extract values for rates, means, or probabilities, together with original data

• tidy() Extract a one-line summary of a model

• mod_pois() Specify a Poisson model

• mod_binom() Specify a binomial model

• mod_norm() Specify a normal model

• fit() Fit a model

• is_fitted() See if a model has been fitted

• unfit() Reset a model

Examples

set.seed(0)

## specify model
mod <- mod_pois(injuries ~ age + sex + year,
                data = nzl_injuries,
                exposure = popn)

## extract prior distribution
## of hyper-parameters
mod |>
  components()

## fit model
mod <- mod |>
  fit()

## extract posterior distribution
## of hyper-parameters
mod |>
  components()

## fit normal model
mod <- mod_norm(value ~ age * diag + year,
                data = nld_expenditure,
                weights = 1) |>
  fit()

## dispersion (= standard deviation in normal model)
## on the transformed scale
mod |>
  components() |>
  subset(component == "disp")

## disperson on the original scale
mod |>
  components(original_scale = TRUE) |>
  subset(component == "disp")

bage documentation built on Aug. 8, 2025, 6:09 p.m.