pc-cor1: Utility functions for the PC prior for correlation in AR(1)

In inbo/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 correlation in the Gaussian AR(1) model where the base-model is correlation one.

Usage

 inla.pc.rcor1(n, u, alpha, lambda)
 inla.pc.dcor1(cor, u, alpha, lambda, log = FALSE)
 inla.pc.qcor1(p, u, alpha, lambda)
 inla.pc.pcor1(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)
cor	Vector of correlations
log	Logical. Return the density in natural or log-scale.
p	Vector of probabilities
q	Vector of quantiles

Details

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

Value

inla.pc.dcor1 gives the density, inla.pc.pcor1 gives the distribution function, inla.pc.qcor1 gives the quantile function, and inla.pc.rcor1 generates random deviates.

Author(s)

Havard Rue [e-mail]

See Also

inla.doc("pc.rho1")

Examples

 cor = inla.pc.rcor1(100,  lambda = 1)
 d = inla.pc.dcor1(cor, lambda = 1)
 cor = inla.pc.qcor1(c(0.3, 0.7), u = 0.5, alpha=0.75)
 inla.pc.pcor1(cor, u = 0.5, alpha=0.75)
 

inbo/INLA documentation built on Dec. 6, 2019, 9:51 a.m.