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Count-cumulative observation process

Source: R/04_cumulative_class.R
cumulative_process.Rd

Configures models for revision streams that publish cumulative levels. The target is finite-horizon database retention C_t(H), not biological truth. The retraction mechanism is the collapsed kernel h_R(l) = retraction_mass * g_R(l); it does not separately identify a truth probability and a conditional revision-delay law.

Usage

cumulative_process(
  observation = c("hurdle_ztnb", "hurdle_ztpoisson", "cumulative"),
  retraction_delay = lognormal_delay(),
  settlement = 26L,
  retraction_mass = beta_prior(1.5, 20),
  movement_intercept = normal_prior(-1, 2),
  movement_age = normal_prior(0, 1),
  movement_previous = normal_prior(0, 1),
  magnitude_size = NULL
)

Arguments

observation

Observation composite likelihood. "cumulative" uses cumulative Poisson or negative-binomial marginals according to the model's likelihood. "hurdle_ztnb" uses signed hurdle updates with a zero-truncated-negative-binomial magnitude. "hurdle_ztpoisson" uses the corresponding zero-truncated-Poisson magnitude.

retraction_delay

Parametric delay family for retraction ages 1:H. Lognormal, gamma, and generalized gamma are supported.

settlement

Positive integer settlement horizon H, in model steps.

retraction_mass

Prior or fixed value in [0, 1] for the finite-horizon mass of h_R. A Beta prior is used by default.

movement_intercept, movement_age, movement_previous

Priors or fixed values for the bounded movement-probability regression. Set movement_previous = 0 to disable previous-movement dependence.

magnitude_size

Positive prior or fixed value for the ZTNB magnitude size. It is used only by "hurdle_ztnb".

Value

A cumulative_process_class object for model(cumulative = ).

Examples

cumulative_process()
#> Observation: Signed hurdle--ZTNB update composite
#> Settlement horizon: H = 26 model steps
#> Retraction kernel: h_R(l) = mass * LogNormal(l)
#> Retraction mass: mass ~ Beta( 1.5, 20.0)
#> Movement: intercept ~ Normal(-1, 2), age ~ Normal(0, 1), previous ~ Normal(0,
#> 1)
#> Magnitude size: size ~ LogNormal(0.0, 1.5)
cumulative_process(observation = "cumulative", settlement = 52L)
#> Observation: Cumulative-level composite
#> Settlement horizon: H = 52 model steps
#> Retraction kernel: h_R(l) = mass * LogNormal(l)
#> Retraction mass: mass ~ Beta( 1.5, 20.0)
cumulative_process(observation = "hurdle_ztpoisson", settlement = 6L)
#> Observation: Signed hurdle--ZTPoisson update composite
#> Settlement horizon: H = 6 model steps
#> Retraction kernel: h_R(l) = mass * LogNormal(l)
#> Retraction mass: mass ~ Beta( 1.5, 20.0)
#> Movement: intercept ~ Normal(-1, 2), age ~ Normal(0, 1), previous ~ Normal(0,
#> 1)