pc-prec: Utility functions for the PC prior for the precision

In andrewzm/INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion

Description

Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the precision in the Gaussian distribution.

Usage

 inla.pc.rprec(n, u, alpha, lambda)
 inla.pc.dprec(prec, u, alpha, lambda, log = FALSE)
 inla.pc.qprec(p, u, alpha, lambda)
 inla.pc.pprec(q, u, alpha, lambda)
 

Arguments

n	Number of observations
u	The upper limit (see Details)
alpha	The probability going above the upper limit (see Details)
lambda	The rate parameter (see Details)
prec	Vector of precisions
log	Logical. Return the density in natural or log-scale.
p	Vector of probabilities
q	Vector of quantiles

Details

The statement Prob(1/sqrt(prec) > u) = alpha is used to determine lambda unless lambda is given. Either lambda must be given, or u AND alpha.

Value

inla.pc.dprec gives the density, inla.pc.pprec gives the distribution function, inla.pc.qprec gives the quantile function, and inla.pc.rprec generates random deviates.

Author(s)

Havard Rue [e-mail]

See Also

inla.doc("pc.prec")

Examples

 prec = inla.pc.rprec(100,  lambda = 1)
 d = inla.pc.dprec(prec, lambda = 1)
 prec = inla.pc.qprec(0.5, u = 1, alpha=0.01)
 inla.pc.pprec(prec, u = 1, alpha=0.01)
 

andrewzm/INLA documentation built on May 10, 2019, 11:12 a.m.