pc.vminf {INLA} | R Documentation

## Utility functions for the PC prior for the concentration of von Mises distribution with point mass base model

### Description

Functions to evaluate, sample, compute quantiles and percentiles of the PC prior for the concentration in the von Mises distribution.

### Usage

    inla.pc.rvminf(n, u, alpha, lambda)

    inla.pc.dvminf(k, u, alpha, lambda, log = FALSE)

    inla.pc.qvminf(p, u, alpha, lambda, len = 2048L)

    inla.pc.pvminf(q, u, alpha, lambda, log = FALSE)

### Arguments

`n` | Number of observations
`u` | The upper limit (0 < u < 2*pi). The small values of u indicate a high concentration to a point mass, whilst large values of u mean that the user believes the data spread widely.
`alpha` | The probability going above the upper limit (the probability assigned to the event Prob(2*pi/(1+k) > u)).
`lambda` | The rate parameter.
`k` | The concentration of von Mises distribution
`log` | Logical. Return the density in natural or log-scale.
`p` | Vector of probabilities.
`q` | Vector of quantiles.

### Details

The statement `Prob(2*pi/(1+k) > u) = alpha` is used to determine `lambda` unless `lambda` is given. Either `lambda` must be given, or `u` AND `alpha`.

Due to limitations in handling extreme values for special functions, the output of these functions may exhibit bias when the input parameter values are either excessively large or very close to zero.

### Value

`inla.pc.dvminf` gives the density, `inla.pc.pvminf` gives the distribution function, `inla.pc.qvminf` gives the quantile function, and `inla.pc.rvminf` generates random deviates.

### Author(s)

Xiang Ye xiang.ye@kaust.edu.sa

### See Also

inla.doc("pc.vminf")

### Examples

     k = inla.pc.rvminf(100,  lambda = 1)
     d = inla.pc.dvminf(1, lambda = 1)
     k = inla.pc.qvminf(0.5, u = 1, alpha=0.01)
     inla.pc.pvminf(5, u = 1, alpha=0.01)

[Package INLA version 26.08.22 Index]