Description Usage Arguments Details See Also Examples
View source: R/transitionPriors.R
Build a path specific Weibull TransitionPriors object, with independent gamma priors for the shape/scale parameters of the latent and infectious time distributions.
1 2 3 4 5 6 7 8 9 10 | WeibullTransitionPriors(
latent_shape_prior_alpha,
latent_shape_prior_beta,
latent_scale_prior_alpha,
latent_scale_prior_beta,
infectious_shape_prior_alpha,
infectious_shape_prior_beta,
infectious_scale_prior_alpha,
infectious_scale_prior_beta
)
|
latent_shape_prior_alpha |
The alpha (shape) value for the gamma hyperprior on the latent period shape parameter |
latent_shape_prior_beta |
The beta (rate) value for the gamma hyperprior on the latent period shape parameter |
latent_scale_prior_alpha |
The alpha (shape) value for the gamma hyperprior on the latent period scale parameter |
latent_scale_prior_beta |
The beta (rate) value for the gamma hyperprior on the latent period scale parameter |
infectious_shape_prior_alpha |
The alpha (shape) value for the gamma hyperprior on the infectious period shape parameter |
infectious_shape_prior_beta |
The beta (rate) value for the gamma hyperprior on the infectious period shape parameter |
infectious_scale_prior_alpha |
The alpha (shape) value for the gamma hyperprior on the infectious period scale parameter |
infectious_scale_prior_beta |
The beta (rate) value for the gamma hyperprior on the infectious period scale parameter |
WeibullTransitionPriors assumes that both the latent and infectious times are distributed according to Weibull distributions. Let τ_{lat} and τ_{inf} represent random variables corresponding to latent and infectious times. We then have: τ_{lat} \sim Weibull(θ_1^{lat}, θ_2^{lat}) τ_{inf} \sim Weibull(θ_1^{inf}, theta_2^{inf}) For each of these four parameters, we assign a gamma prior, parameterized by shape and rate: f(θ_{.}^{.}| α, β^{-1}) = frac{1}{Γ(α) β^α}e^{-\frac{x}{β}}x^{α-1}
These hyperparameters are specified by the eight arguments to WeibullTransitionPriors.
TransitionPriors
, PathSpecificTransitionPriors
,
ExponentialTransitionPriors
1 | transitionPriors <- WeibullTransitionPriors(35,20,150,20,90,20,220,20)
|
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