pc-alphaw: Utility functions for the PC prior for the 'alpha' parameter...

In INBO-BMK/INLA: Full Bayesian Analysis of Latent Gaussian Models using Integrated Nested Laplace Approximations

Description

Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the alpha parameter in the Weibull likelihood

Usage

 inla.pc.ralphaw(n, lambda = 5)
 inla.pc.dalphaw(alpha, lambda = 5, log = FALSE)
 inla.pc.qalphaw(p, lambda = 5)
 inla.pc.palphaw(q, lambda = 5)
 

Arguments

n	Number of observations
lambda	The rate parameter in the PC-prior
alpha	Vector of evaluation points, where alpha>0.
log	Logical. Return the density in natural or log-scale.
p	Vector of probabilities
q	Vector of quantiles

Details

This gives the PC prior for the alpha parameter for the Weibull likelihood, where alpha=1 is the base model.

Value

inla.pc.dalphaw gives the density, inla.pc.palphaw gives the distribution function, inla.pc.qalphaw gives the quantile function, and inla.pc.ralphaw generates random deviates.

Author(s)

Havard Rue [e-mail]

See Also

inla.doc("pc.alphaw")

Examples

 x = inla.pc.ralphaw(100,  lambda = 5)
 d = inla.pc.dalphaw(x, lambda = 5)
 x = inla.pc.qalphaw(0.5, lambda = 5)
 inla.pc.palphaw(x, lambda = 5)
 

INBO-BMK/INLA documentation built on Dec. 4, 2019, 11:43 p.m.