| 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)