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Create Smoothing Structure Specification with Priors

Source: R/smoothing_dispersion_structure.r
smoothing_structure.Rd

This function creates a standardized smoothing structure object that specifies both the smoothing structure and associated priors for EpiStrainDynamics models. tau (denoted \(\rho\) in Eales et al. 2022, Epidemics) is the smoothing parameter that penalises how much the underlying trend's growth rate is allowed to change over time. Smaller values enforce a smoother trend; larger values allow it to bend more sharply.

Usage

smoothing_structure(smoothing_type = "shared", tau_mean = NULL, tau_sd = NULL)

Arguments

smoothing_type

Character string specifying the smoothing type:

  • "shared": All pathogens have the same smoothing parameter (equivalent to tau[1]). By default a model with a single pathogen will have shared smoothing type.

  • "independent": Independent smoothing per pathogen (equivalent to tau[number of pathogens])

  • "correlated": Correlated smoothing type (equivalent to Sigma[number of pathogens, number of pathogens])

tau_mean

Optional numeric vector specifying the prior mean(s) for tau parameter. Can be provided for shared (single value) and independent smoothing types (can provide a single value which will be repeated for each pathogen or can provide a unique prior for each pathogen). Prior for tau for correlated smoothing type is not currently supported.

tau_sd

Numeric vector specifying the prior standard deviation(s) for tau parameter. Can be provided for shared (single value) and independent smoothing types (can provide a single value which will be repeated for each pathogen or can provide a unique prior for each pathogen). Prior for tau for correlated smoothing type is not currently supported.

Value

An object of class EpiStrainDynamics.smoothing containing:

smoothing_type

The specified smoothing structure type

tau_priors

Prior specifications for tau

priors_provided

Integer flag passed to the Stan model: 1 if no priors were supplied (Stan's built-in default prior is used), 2 if priors were supplied (the tau_mean/tau_sd values are used as the prior)

Examples

# Shared smoothing with scalar priors
shared_smooth <- smoothing_structure("shared", tau_mean = 0, tau_sd = 1)

# Independent smoothing with vector priors
indep_smooth <- smoothing_structure("independent",
  tau_mean = c(0, 0, 0),
  tau_sd = c(1, 1, 1)
)