RW2_AR: Second-Order Random Walk Prior with Autoregressive Errors

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

Second-Order Random Walk Prior with Autoregressive Errors

Description

Use one or more second-order random walks, combined with an autoregressive error term, to model a main effect or an interaction. Typically used with time.

Usage

RW2_AR(
  s_rw = 1,
  sd = 1,
  sd_slope = 1,
  n_coef = 2,
  s_ar = 1,
  shape1 = 5,
  shape2 = 5,
  along = NULL,
  con = c("none", "by")
)

Arguments

s_rw	Scale for the innovations in the RW2 process. Default is 1.
sd	Standard deviation for initial term in RW2 process. Default is 1. Can be 0.
sd_slope	Standard deviation in the prior for the initial slope of the RW2 process. Larger values imply steeper slopes. Default is 1.
n_coef	Number of lagged terms in the model, ie the order of the model. Default is 2.
s_ar	Scale for the innovations in the AR process. Default is 1.
shape1, shape2	Parameters for beta-distribution prior for coefficients. Defaults are 5 and 5.
along	Name of the variable to be used as the 'along' variable. Only used with interactions.
con	Constraints on parameters. Current choices are "none" and "by". Default is "none". See below for details.

Details

If RW2_AR() is used with an interaction, separate random walks are constructed along the 'along' variable, within each combination of the 'by' variables.

Parameters controlling the RW2 process:

• s_rw

• sd

• sd_slope

Parameters controlling the AR process:

• n_coef

• s_ar

• shape1

• shape2

Value

An object of class "bage_prior_rw2randomar" or "bage_prior_rw2zeroar".

Mathematical details

When RW2_AR() is used with a main effect,

\beta_j = \alpha_j + \epsilon_j

\alpha_1 \sim \text{N}(0, \mathtt{sd}^2)

\alpha_2 \sim \text{N}(\alpha_1, \mathtt{sd\_slope}^2)

\alpha_j \sim \text{N}(2\alpha_{j-1} - \alpha_{j-2}, \tau^2), \quad j = 3, \cdots, J

\epsilon_j = \phi_1 \epsilon_{j-1} + \cdots + \phi_{\mathtt{n\_coef}} \epsilon_{j-\mathtt{n\_coef}} + \varepsilon_j

\varepsilon_j \sim \text{N}(0, \omega^2),

and when it is used with an interaction,

\beta_{u,v} = \alpha_{u,v} + \epsilon_{u,v}

\alpha_{u,1} \sim \text{N}(0, \mathtt{sd}^2)

\alpha_{u,2} \sim \text{N}(\alpha_{u,1}, \mathtt{sd\_slope}^2)

\alpha_{u,v} \sim \text{N}(2\alpha_{u,v-1} - \alpha_{u,v-2}, \tau^2), \quad v = 3, \cdots, V

\epsilon_{u,v} = \phi_1 \epsilon_{u,v-1} + \cdots + \phi_{\mathtt{n\_coef}} \epsilon_{u,v-\mathtt{n\_coef}} + \varepsilon_{u,v}

\varepsilon_{u,v} \sim \text{N}(0, \omega^2),

where

• \pmb{\beta} is the main effect or interaction;

• j denotes position within the main effect;

• u denotes position within the 'by' variable(s) of the interaction; and

• v denotes position within the 'along' variable of the interaction.

The \tau parameter in the random walk has prior

\tau \sim \text{N}^+(0, \mathtt{s\_rw}^2)

Internally, RW2_AR() derives a value for \omega that gives \epsilon_j or \epsilon_{u,v} a marginal variance of \nu^2. Parameter \nu has a half-normal prior

\nu \sim \text{N}^+(0, \mathtt{s\_ar}^2).

The correlation coefficients \phi_1, \cdots, \phi_{\mathtt{n\_coef}} each have prior

0.5 \phi_k - 0.5 \sim \text{Beta}(\mathtt{shape1}, \mathtt{shape2}).

Constraints

With some combinations of terms and priors, the values of the intercept, main effects, and interactions are only weakly identified. This weak identifiability is typically harmless. However, in some applications, such as when trying to obtain interpretable values for main effects and interactions, it can be helpful to increase identifiability through the use of constraints, specified through the con argument.

Current options for con are:

• "none" No constraints. The default.

• "by" Only used in interaction terms that include 'along' and 'by' dimensions. Within each value of the 'along' dimension, terms across each 'by' dimension are constrained to sum to 0.

See Also

• RW2_AR1() Special case of RW2_AR()

• RW2() Second-order random walk

• AR() AR process

• priors Overview of priors implemented in bage

• set_prior() Specify prior for intercept, main effect, or interaction

• Mathematical Details vignette

Examples

RW2_AR()
RW2_AR(sd_slope = 2, n_coef = 3, s_ar = 0.5)

bage documentation built on May 20, 2026, 9:10 a.m.