mcmc_mix3 returns the posterior samples of the parameters, for fitting the 3-component discrete extreme value mixture distribution. The samples are obtained using Markov chain Monte Carlo (MCMC).
mcmc_mix3(
x,
count,
v_set,
u_set,
v,
u,
alpha1,
theta1,
alpha2,
theta2,
shape,
sigma,
a_psi1,
a_psi2,
a_psiu,
b_psiu,
a_alpha1,
b_alpha1,
a_theta1,
b_theta1,
a_alpha2,
b_alpha2,
a_theta2,
b_theta2,
m_shape,
s_shape,
a_sigma,
b_sigma,
powerlaw1,
positive1,
positive2,
a_pseudo,
b_pseudo,
pr_power2,
iter,
thin,
burn,
freq,
invt,
mc3_or_marg = TRUE
)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 v will be sampled from
Positive integer vector of the values u will be sampled from
Positive integer, initial value of the lower threshold
Positive integer, initial value of the upper threshold
Real number greater than 1, 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
Real number in (0, 1], initial value of the parameter
Real number, initial value of the parameter
Positive real number, 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 the discrete power law assumed for below v?
Boolean, is alpha1 positive (TRUE) or unbounded (FALSE)?
Boolean, is alpha2 positive (TRUE) or unbounded (FALSE)?
Positive real number, first parameter of the pseudoprior beta distribution for theta2 in model selection; ignored if pr_power2 = 1.0
Positive real number, second parameter of the pseudoprior beta distribution for theta2 in model selection; ignored if pr_power2 = 1.0
Real number in [0, 1], prior probability of the discrete power law (between v and u)
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)?
In the MCMC, a componentwise Metropolis-Hastings algorithm is used. The thresholds v and u are treated as parameters and therefore sampled. The hyperparameters are used in the following priors: psi1 / (1.0 - psiu) ~ Beta(a_psi1, a_psi2); 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); theta2 ~ Beta(a_theta2, b_theta2); shape ~ Normal(mean = m_shape, sd = s_shape); sigma ~ Gamma(a_sigma, scale = b_sigma). If pr_power2 = 1.0, the discrete power law (between v and u) is assumed, and the samples of theta2 will be all 1.0. If pr_power2 is in (0.0, 1.0), model selection between the polylog distribution and the discrete power law will be performed within the MCMC.
mcmc_pol and mcmc_mix2 for MCMC for the Zipf-polylog and 2-component discrete extreme value mixture distributions, respectively.