vm_posterior: Posterior of the von Mises distribution.

In keesmulder/circbayes: Bayesian Analysis of Circular Data

Description

Posterior of the von Mises distribution.

Usage

vm_posterior(th, prior = c(0, 0, 0), niter = 1000, ...)

## S3 method for class 'vm_posterior_mod'
print(x, digits = 3, ...)

## S3 method for class 'vm_posterior_mod'
coef(object, ...)

## S3 method for class 'vm_posterior_mod'
posterior_samples(object, ...)

## S3 method for class 'vm_posterior_mod'
marg_lik(x, method = "integrate", ...)

## S3 method for class 'vm_posterior_mod'
plot(x, ...)

Arguments

th	Circular observations, either numeric in radians, or circular.
prior	Numeric of length 3. Prior parameters for conjugate prior of the von Mises distribution. The order is μ_0, R_0, c.
niter	Number of iterations to perform MCMC for.
...	Further arguments passed to circglmbayes::circGLM.

Value

Object of type vm_posterior_mod.

Methods (by generic)

• print: Print function.

• coef: Posterior summary of parameters.

• posterior_samples: Posterior samples (only model parameters, not derived).

• marg_lik: Compute marginal likelihood.

• plot: Plot function.

Examples

vm_posterior(rvm(30, 2, 5))

keesmulder/circbayes documentation built on May 30, 2019, 2:04 p.m.