vm_mix: Posterior of mixture of Von Mises distributions.
In keesmulder/circbayes: Bayesian Analysis of Circular Data

Description Usage Arguments Value Examples

Posterior of mixture of Von Mises distributions.

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vm_mix(th, n_comp = 3, mu_logprior_fun = function(mu) 0,
  kp_logprior_fun = function(kp) 0, alph_prior_param = rep(1, n_comp),
  fixed_pmat = matrix(NA, n_comp, 4), niter = 1000, ...)

`th`	Circular observations, either `numeric` in radians, or `circular`.
`n_comp`	Integer; Number of mixture components.
`mu_logprior_fun`	Function; A function with a single argument, which returns the log of the prior probability of μ. Defaults to a uniform prior function.
`kp_logprior_fun`	Function; A function with a single argument, which returns the log of the prior probability of κ. Defaults to a uniform prior function. In contrast to the other parameters, for κ the constant (uniform) prior is improper.
`alph_prior_param`	Integer vector; The mixture weight parameter vector α is given its conjugate Dirichlet prior. The default is `rep(1, n_comp)`, which is the noninformative uniform prior over the `n_comp` simplex.
`fixed_pmat`	A numeric matrix with `n_comp` rows and four columns, corresponding to μ, κ, λ, α, in that order. Any element that is not `NA` in this matrix will be held constant at the given value and not sampled.
`niter`	Number of iterations to perform MCMC for.
`...`	Further arguments passed to `flexcircmix::fitbatmix`.