Delay distribution for the Bayesian Nowcast — delay

Specify the reporting-delay distribution. Parameter slots accept a fixed numeric, a prior_class (e.g. normal_prior()), or numeric(0) for the default prior.

Usage

lognormal_delay(
  mu = numeric(0),
  sigma = numeric(0),
  num_delay_seasons = 1L,
  season_distribution = std_normal_prior()
)

gamma_delay(
  shape = numeric(0),
  rate = numeric(0),
  num_delay_seasons = 1L,
  season_distribution = std_normal_prior()
)

generalized_gamma_delay(
  mu = numeric(0),
  sigma = numeric(0),
  Q = numeric(0),
  num_delay_seasons = 1L,
  season_distribution = std_normal_prior()
)

dirichlet_delay(alpha = numeric(0), bins = numeric(0))

Arguments

mu: Log-mean intercept (delay_mu).
sigma: Log-scale / SD parameter > 0.
num_delay_seasons: Number of periodic delay seasons. Default 1.
season_distribution: Prior for the delay-season effects.
shape, rate: Gamma delay parameters (the shape slot is the log-mean, the rate slot the delay SD; see the original parameterisation).
Q: GenGamma shape (delay_Q); Q = 0 recovers lognormal.
alpha: Dirichlet concentration (scalar broadcast to all bins).
bins: Dirichlet: number of explicit delay bins (geometric tail beyond).

Value

A delay_process_class object.

Default priors

When a prior argument is left empty, default_priors() supplies these defaults. The delay mean prior is data-informed: a normal_prior() on the log scale, centred at the log of the median observed delay.

LogNormal: mu ~ data-informed normal_prior(log median delay); sigma ~ gamma_prior(2, 2).
Gamma: shape ~ data-informed normal_prior(...); rate ~ gamma_prior(2, 2 / sd) (data-informed SD).
Generalised Gamma: mu ~ data-informed normal_prior(...); sigma ~ gamma_prior(2, 0.1); Q (shape) ~ normal_prior(0, 0.5).
Dirichlet: alpha ~ a per-bin concentration vector, data-informed from the empirical delay pmf (0.05 + (bins+1) * pmf), else rep(1, bins + 1).

Examples

lognormal_delay()
#> LogNormal(mu, sigma)
lognormal_delay(mu = normal_prior(log(7), 0.5))
#> LogNormal(mu ~ Normal(1.946, 0.500), sigma)
gamma_delay()
#> Gamma(shape, rate)
generalized_gamma_delay(Q = 1)
#> GeneralizedGamma(mu, sigma, Q = "1")
dirichlet_delay(alpha = 1, bins = 21)
#> Dirichlet(alpha = "1"; bins = "21")