mcmc_mix1 returns the posterior samples of the parameters, for fitting the TZP-power-law mixture distribution. The samples are obtained using Markov chain Monte Carlo (MCMC).
mcmc_mix1(
x,
count,
u_set,
u,
alpha1,
theta1,
alpha2,
a_psiu,
b_psiu,
a_alpha1,
b_alpha1,
a_theta1,
b_theta1,
a_alpha2,
b_alpha2,
positive,
iter,
thin,
burn,
freq,
invt,
mc3_or_marg,
x_max
)A list: $pars is a data frame of iter rows of the MCMC samples, $fitted is a data frame of length(x) rows with the fitted values, amongst other quantities related to the MCMC
Vector of the unique values (positive integers) of the data
Vector of the same length as x that contains the counts of each unique value in the full data, which is essentially rep(x, count)
Positive integer vector of the values u will be sampled from
Positive integer, initial value of the threshold
Real number, initial value of the parameter
Real number in (0, 1], initial value of the parameter
Real number greater than 1, initial value of the parameter
Scalars, real numbers representing the hyperparameters of the prior distributions for the respective parameters. See details for the specification of the priors.
Boolean, is alpha positive (TRUE) or unbounded (FALSE)?
Positive integer representing the length of the MCMC output
Positive integer representing the thinning in the MCMC
Non-negative integer representing the burn-in of the MCMC
Positive integer representing the frequency of the sampled values being printed
Vector of the inverse temperatures for Metropolis-coupled MCMC
Boolean, is invt for parallel tempering / Metropolis-coupled MCMC (TRUE, default) or marginal likelihood via power posterior (FALSE)?
Scalar, positive integer limit for computing the normalising constant
In the MCMC, a componentwise Metropolis-Hastings algorithm is used. The threshold u is treated as a parameter and therefore sampled. The hyperparameters are used in the following priors: u is such that the implied unique exceedance probability psiu ~ Uniform(a_psi, b_psi); alpha1 ~ Normal(mean = a_alpha1, sd = b_alpha1); theta1 ~ Beta(a_theta1, b_theta1); alpha2 ~ Normal(mean = a_alpha2, sd = b_alpha2)
mcmc_pol, mcmc_mix2 and mcmc_mix3 for MCMC for the Zipf-polylog, and 2-component and 3-component discrete extreme value mixture distributions, respectively.